Single Document Summarization

Nowadays, automatic multidocument text summarization systems can successfully retrieve the summary sentences from the input documents. But, it has many limitations such as inaccurate extraction to essential sentences, low coverage, poor coherence among the sentences, and redundancy. This paper introduces a new concept of timestamp approach with Naïve Bayesian Classification approach for multidocument text summarization. The timestamp provides the summary an ordered look, which achieves the coherent looking summary. It extracts the more relevant information from the multiple documents. Here, scoring strategy is also used to calculate the score for the words to obtain the word frequency. The higher linguistic quality is estimated in terms of readability and comprehensibility. In order to show the efficiency of the proposed method, this paper presents the comparison between the proposed methods with the existing MEAD algorithm. The timestamp procedure is also applied on the MEAD algori...

With the problem of extended information resources and the remarkable evaluate of data removal, the require of having automated summarization techniques revealed up. As summarization is needed the most at present searching information on the internet, where the user moves for a specific space of passion as per his question, area centered on summaries would provide the best. Ontology based summarization system for is provided. The ontology is a subjective model, which gives the important framework for semantic representation of textual data. In our suggested system implement the hierarchical levels of ontology to even more enhance the high summary and to execute hierarchical text classification in the field of earth quake management. We signify a scientific study of different techniques in which ontology has been applied for summarization practice. Comprehensive experiments on a selection of press launch appropriate to 2011 Sikkim earth quake illustrate that ontology centered multipl...

In this paper a methodology to mine the concepts from documents and use these concepts to generate an objective summary of the claims section of the patent documents is proposed. Conceptual Graph (CG) formalism as proposed by Sowa (Sowa 1984) is used in this work for representing the concepts and their relationships. Automatic identification of concepts and conceptual relations from text documents is a challenging task. In this work the focus is on the analysis of the patent documents, mainly on the claim's section (Claim) of the documents. There are several complexities in the writing style of these documents as they are technical as well as legal. It is observed that the general in-depth parsers available in the open domain fail to parse the 'claims section' sentences in patent documents. The failure of in-depth parsers has motivated us, to develop methodology to extract CGs using other resources. Thus in the present work shallow parsing, NER and machine learning technique for extracting concepts and conceptual relationships from sentences in the claim section of patent documents is used. Thus, this paper discusses i) Generation of CG, a semantic network and ii) Generation of abstractive summary of the claims section of the patent. The aim is to generate a summary which is 30% of the whole claim section. Here we use Restricted Boltzmann Machines (RBMs), a deep learning technique for automatically extracting CGs. We have tested our methodology using a corpus of 5000 patent documents from electronics domain. The results obtained are encouraging and is comparable with the state of the art systems.

In this paper a methodology to mine the concepts from documents and use these concepts to generate an objective summary of the claims section of the patent documents is proposed. Conceptual Graph (CG) formalism as proposed by Sowa (Sowa 1984) is used in this work for representing the concepts and their relationships. Automatic identification of concepts and conceptual relations from text documents is a challenging task. In this work the focus is on the analysis of the patent documents, mainly on the claim's section (Claim) of the documents. There are several complexities in the writing style of these documents as they are technical as well as legal. It is observed that the general in-depth parsers available in the open domain fail to parse the 'claims section' sentences in patent documents. The failure of in-depth parsers has motivated us, to develop methodology to extract CGs using other resources. Thus in the present work shallow parsing, NER and machine learning technique for extracting concepts and conceptual relationships from sentences in the claim section of patent documents is used. Thus, this paper discusses i) Generation of CG, a semantic network and ii) Generation of abstractive summary of the claims section of the patent. The aim is to generate a summary which is 30% of the whole claim section. Here we use Restricted Boltzmann Machines (RBMs), a deep learning technique for automatically extracting CGs. We have tested our methodology using a corpus of 5000 patent documents from electronics domain. The results obtained are encouraging and is comparable with the state of the art systems.

As long as the internet user is increasing, online electronic content is growing proportionally irrespective of languages. A lot of research works on English text summarization have come to light to deal with this gigantic body of online text. Unfortunately, a few works have been accomplished for Bangla though a huge number of people are involved with this language. This article has tried to explore the trend of research work on Bangla text summarization. Fourteen approaches have been briefly expounded here by addressing the pros and cons with some scope of improvement. A comparison has also been turned based on their incorporated features and evaluation results. It is expected that this article will draw the attention of more researchers in the area of Bangla text summarization and give a crystal-clear message about the opportunities to the next generation. The integrated message about all the existing methods has been depicted here to reveal the importance of Bangla text summariza...

The objective of this paper is to generate opinion summaries from crawled product reviews extracted automatically from Amazon.com and processed using featurebased sparse non negative factorization technique. The required features for generation of summaries are extracted from the online reviews. As the number of online reviews is large, the feature space is huge which would increase the computational complexity of the opinion summary generation algorithm. This necessitates the reduction of feature space which is done efficiently by using SentiWordNet. Feature-Based Sparse Non-Negative Matrix Factorization method (FS-NMF) is proposed to ensemble the reviews into feature relevant clusters which are based on non-negative matrix factorization (NMF) framework. Extensive case studies, experiments, and summary evaluation on ROUGE demonstrate the effectiveness of this system.

We address extractive summarization of technical documents in the oil and gas industry, a major and urgent task due to the large volume of critical reports in that industry. We examine five distinct state-of-the-art extractive algorithms; to assess performance, a new open dataset was created using the open access Journal of Petroleum Exploration and Production Technology (JPEPT). Abstracts for papers in this journal were used as ground truths for summarization. Algorithms were refined to work with these documents in the best possible way. Our most effective algorithm achieved a state-of-the-art ROUGE-2 score of 0.123, taking 83 minutes to summarize the entire JPEPT dataset.

Nowadays, the amount of Arabic documents has increased significantly in different domains, such as news articles, emails, business summary, biomedicine, web sites and social media documents. Some databases have increased in its size to terabyte. Multi-document summarization is the method of creating a summary of a group of interrelated documents. Therefore, the rise of the desire for Arabic multi documents text summarization (at the instant rates possible, coherent, grammatical and meaningful sentences) is increased. Recently, many efforts on multi-document text summarization that is related to the English language have been performed. Arabic multi-document summarization is remained on its early stages. Consequently, the researchers in this paper propose an Arabic Multi-Document Text Summarization (AMD-TS) model based on parallel computing techniques. This model of Arabic text summarization could effectively and rapidly summarize Arabic multi-documents in real time. A conceptual framework is proposed based on published researches dealing with text summarization techniques of different languages. The proposed model creates an accurate, coherent and complete Arabic multi-document text summarization model. The dataset that is used in the investigation stage is derived from different domains, such as education, sports and politics. This dataset contains texts of various sizes. The experiments are then designed to be on specific domain (news articles domain). In order to increase the summarization process efficiency and performance, the researchers in this paper use parallel computing. The model covers the deficiency of Arabic Automatic Summarization Systems (ASS) by enhancing the final summary.

Multi‐document summarization is a process of automatic creation of a compressed version of a given collection of documents that provides useful information to users. In this article we propose a generic multi‐document summarization method based on sentence clustering. We introduce five clustering methods, which optimize various aspects of intra‐cluster similarity, inter‐cluster dissimilarity and their combinations. To solve the clustering problem a modification of discrete particle swarm optimization algorithm has been proposed. The experimental results on open benchmark data sets from DUC2005 and DUC2007 show that our method significantly outperforms the baseline methods for multi‐document summarization.

We focus on the practical issue of designing laboratory activities, concentrating on identifying key components necessary to insure quality and usefulness in the on-line SIGCSE Computing Laboratory Repository. We summarize the current capabilities and future extensions of the Repository, characterize a variety of qualities necessary and recommended for successful laboratory activities, identify a standard cover page for all lab activity submissions, and specify some guidelines for referees of submitted materials. Finally, we have designed four sample laboratory projects. Their contents are summarized in this paper and the completed projects will be available from the Repository after student testing in the Fall of 1997, if not sooner.

Multi document summarization has very great impact among research community, ever since the growth of online information and availability. Selecting most important sentences from such huge repository of data is quiet tricky and challenging task. While multi document poses some additional overhead in sentence selection, generating summaries for each individual documents and merging the sentences in a coherent order would greater strength. The proposed approach was competitively better as compared to state of MEAD summarizer at focused compression ratios. This paper focus on three different studies namely i. To find the performance of multi document summarizer from single document cluster (using MEAD) ii. Comparison of our approach with MEAD performance for the dataset considered iii. To extract sentences for multi document summarization at 30% compression rate to obtain 100% efficiency using 7-point summary sheet. Investigation carried out from an average of 22 documents shows that our system is promising.

The performance of most metaheuristic algorithms depends on parameters whose settings essentially serve as a key function in determining the quality of the solution and the efficiency of the search. A trend that has emerged recently is to make the algorithm parameters automatically adapt to different problems during optimization, thereby liberating the user from the tedious and time-consuming task of manual setting. These fine-tuning techniques continue to be the object of ongoing research. Differential evolution (DE) is a simple yet powerful population-based metaheuristic. It has demonstrated good convergence, and its principles are easy to understand. DE is very sensitive to its parameter settings and mutation strategy; thus, this study aims to investigate these settings with the diverse versions of adaptive DE algorithms. This study has two main objectives: (1) to present an extension for the original taxonomy of evolutionary algorithms (EAs) parameter settings that has been overlooked by prior research and therefore minimize any confusion that might arise from the former taxonomy and (2) to investigate the various algorithmic design schemes that have been used in the different variants of adaptive DE and convey them in a new classification style. In other words, this study describes in depth the structural analysis and working principle that underlie the promising and recent work in this field, to analyze their advantages and disadvantages and to gain future insights that can further improve these algorithms. Finally, the interpretation of the literature and the comparative analysis of the results offer several guidelines for designing and implementing adaptive DE algorithms. The proposed design framework provides readers with the main steps required to integrate any proposed meta-algorithm into parameter and/or strategy adaptation schemes.

received her Ph.D. and M.S.T. degrees in physics in 2007 and 2005 (respectively) from the University of Maine, with research emphasis in physics education. Her B.A. in physics from Grinnell College in 2002 involved research in computer science education. Her current research interests include identity development in undergraduate STEM majors, time-evolution of student understanding, and community practices in physics and physics education. In 2012 and 2013, she and her collaborators received NSF WIDER awards for building faculty resources for assessment on the Physics Education Research User's Guide.

The exponential growth of online textual data triggered the crucial need for an effective and powerful tool that automatically provides the desired content in a summarized form while preserving core information. In this paper, we propose an automatic, generic, and extractive Arabic single document summarizing method aiming at producing a sufficiently informative summary. The proposed extractive method evaluates each sentence based on a combination of statistical and semantic features in which a novel formulation is used taking into account sentence importance, coverage and diversity. Further, two summarizing techniques including score-based and supervised machine learning were employed to produce the summary and then assist leveraging the designed features. We demonstrate the effectiveness of the proposed method through a set of experiments under EASC corpus using ROUGE measure. Compared to some existing related work, the experimental evaluation shows the strength of the proposed method in terms of precision, recall, and F-score performance metrics.

Automatic text summarization has become a relevant topic due to the information overload. This automatization aims to help humans and machines to deal with the vast amount of text data (structured and un-structured) offered on the web and deep web. In this paper a novel approach for automatic extractive text summarization called SENCLUS is presented. Using a genetic clustering algorithm, SENCLUS clusters the sentences as close representation of the text topics using a fitness function based on redundancy and coverage, and applies a scoring function to select the most relevant sentences of each topic to be part of the extractive summary. The approach was validated using the DUC2002 data set and ROUGE summary quality measures. The results shows that the approach is representative against the state of the art methods for extractive automatic text summarization.

Database technology affects many disciplines beyond computer science and business. This paper describes two animations developed with images and color that visually and dynamically introduce fundamental relational database concepts and querying to students of many majors. The goal is for educators in diverse academic disciplines to incorporate the animations in their existing courses, in order to meet their pedagogical needs. The introduction of the animations was assessed and evaluated within several contexts, including non-majors courses offered by computer scientists as well as a course on computational molecular biology. The evaluation indicated that student knowledge of fundamental database concepts improved significantly with the use of the courseware. The animations provide visual learners with an engaging method to learn the topics presented with the ability to replay the dynamic presentation. Overall, the evaluation indicates the accessibility of database topics across the disciplines as well as the specific concepts that need further elaboration.

In the current situation the rate of development of data is growing exponentially in the World Wide Web. Thus, extricating legitimate and valuable data from enormous information has turned into a testing issue. As of late text summarization is perceived as one of the answer for remove applicable data from huge documents. Based on number of documents considered for summarization, the summarization assignment is ordered as single document or multi-document summarization. As opposed to single document, multi-document summarization is all the more trying for the analysts to discover exact synopsis from multiple documents. In this paper we have begun with presentation of multidocument summarization and after that have additionally examined examination and investigation of different methodologies which goes under the multidocument summarization. The paper additionally contains insights about the advantages and issues in the current techniques. This would particularly be useful for scientists working in this field of text information mining. By utilizing this information, scientists can fabricate new or blended based methodologies for multi-document summarization.

It is very difficult for human beings to manually summarize large documents of text. Text summarization solves this problem. Nowadays, Text summarization systems are among the most attractive research areas. Text summarization (TS) is used to provide a shorter version of the original text and keeping the overall meaning. There are various methods that aim to find out well-formed summaries. One of the most commonly used methods is the Latent Semantic Analysis (LSA). In this review, we present a comparative study among almost algorithms based on Latent Semantic Analysis (LSA) approach.

Due to the exponential growth of textual information available on the Web, end users need to be able to access information in summary form-and without losing the most important information in the document when generating the summaries. Automatic generation of extractive summaries from a single document has traditionally been given the task of extracting the most relevant sentences from the original document. The methods employed generally allocate a score to each sentence in the document, taking into account certain features. The most relevant sentences are then selected, according to the score obtained for each sentence. These features include the position of the sentence in the document, its similarity to the title, the sentence length, and the frequency of the terms in the sentence. However, it has still not been possible to achieve a quality of summary that matches that performed by humans and therefore methods continue to be brought forward that aim to improve on the results. This paper addresses the generation of extractive summaries from a single document as a binary optimization problem where the quality (fitness) of the solutions is based on the weighting of individual statistical features of each sentence-such as position, sentence length and the relationship of the summary to the title, combined with group features of similarity between candidate sentences in the summary and the original document, and among the candidate sentences of the summary. This paper proposes a method of extractive single-document summarization based on genetic operators and guided local search, called MA-Sin-gleDocSum. A memetic algorithm is used to integrate the own-population-based search of evolutionary algorithms with a guided local search strategy. The proposed method was compared with the state of the art methods UnifiedRank, DE, FEOM, NetSum, CRF, QCS, SVM, and Manifold Ranking, using ROUGE measures on the datasets DUC2001 and DUC2002. The results showed that MA-SingleDocSum outperforms the state of the art methods.

Due to the exponential growth of textual information available on the Web, end users need to be able to access information in summary form-and without losing the most important information in the document when generating the summaries. Automatic generation of extractive summaries from a single document has traditionally been given the task of extracting the most relevant sentences from the original document. The methods employed generally allocate a score to each sentence in the document, taking into account certain features. The most relevant sentences are then selected, according to the score obtained for each sentence. These features include the position of the sentence in the document, its similarity to the title, the sentence length, and the frequency of the terms in the sentence. However, it has still not been possible to achieve a quality of summary that matches that performed by humans and therefore methods continue to be brought forward that aim to improve on the results. This paper addresses the generation of extractive summaries from a single document as a binary optimization problem where the quality (fitness) of the solutions is based on the weighting of individual statistical features of each sentence-such as position, sentence length and the relationship of the summary to the title, combined with group features of similarity between candidate sentences in the summary and the original document, and among the candidate sentences of the summary. This paper proposes a method of extractive single-document summarization based on genetic operators and guided local search, called MA-Sin-gleDocSum. A memetic algorithm is used to integrate the own-population-based search of evolutionary algorithms with a guided local search strategy. The proposed method was compared with the state of the art methods UnifiedRank, DE, FEOM, NetSum, CRF, QCS, SVM, and Manifold Ranking, using ROUGE measures on the datasets DUC2001 and DUC2002. The results showed that MA-SingleDocSum outperforms the state of the art methods.

Most existing research on applying the matrix factorization approaches to query-focused multi-document summarization (Q-MDS) explores either soft/hard clustering or low rank approximation methods. We employ a different kind of matrix factorization method, namely weighted Archetypal Analysis (wAA) to Q-MDS. In query-focused summarization, given a graph representation of a set of sentences weighted by similarity to the given query, positively and/or negatively salient sentences are values on the weighted data set boundary. We choose to use wAA to compute these extreme values, archetypes, and hence to estimate the importance of sentences in target documents set. We investigate the impact of using the multi-element graph model for query focused summarization via wAA. We conducted experiments on the data of document understanding conference (DUC) 2005 and 2006. Experimental results evidence the improvement of the proposed approach over other closely related methods and many of state-of-the-art systems.

The exponential growth of online textual data triggered the crucial need for an effective and powerful tool that automatically provides the desired content in a summarized form while preserving core information. In this paper, we propose an automatic, generic, and extractive Arabic single document summarizing method aiming at producing a sufficiently informative summary. The proposed extractive method evaluates each sentence based on a combination of statistical and semantic features in which a novel formulation is used taking into account sentence importance, coverage and diversity. Further, two summarizing techniques including score-based and supervised machine learning were employed to produce the summary and then assist leveraging the designed features. We demonstrate the effectiveness of the proposed method through a set of experiments under EASC corpus using ROUGE measure. Compared to some existing related work, the experimental evaluation shows the strength of the proposed method in terms of precision, recall, and F-score performance metrics.

With the problem of extended information resources and the remarkable evaluate of data removal, the require of having automated summarization techniques revealed up. As summarization is needed the most at present searching information on the internet, where the user moves for a specific space of passion as per his question, area centered on summaries would provide the best. Ontology based summarization system for is provided. The ontology is a subjective model, which gives the important framework for semantic representation of textual data. In our suggested system implement the hierarchical levels of ontology to even more enhance the high summary and to execute hierarchical text classification in the field of earth quake management. We signify a scientific study of different techniques in which ontology has been applied for summarization practice. Comprehensive experiments on a selection of press launch appropriate to 2011 Sikkim earth quake illustrate that ontology centered multipl...

Summarizing a text file is the process of constricting source file into a shorter version of the same file conserving its information content and generic meaning of the input source file. Human beings are the most effective mechanism used to generate summary, but if we have to generate a summary for a way too large document in few minutes, in such case automatic text summarizer comes into picture. "Automatic text summarization:" process' source file and generate summary by inputting the source file to the machine (computer) and it returns a summary of the original text file. Text summarization is classified in two methods: extract based summarization & abstract based summarization. In extract based summarization method the input source file is processing in such a way that the important sentences from the document are selected and integrated to form a summarized output file. An abstract based method on the other hand selects the most important sentences & paraphrase th...

As long as the internet user is increasing, online electronic content is growing proportionally irrespective of languages. A lot of research works on English text summarization have come to light to deal with this gigantic body of online text. Unfortunately, a few works have been accomplished for Bangla though a huge number of people are involved with this language. This article has tried to explore the trend of research work on Bangla text summarization. Fourteen approaches have been briefly expounded here by addressing the pros and cons with some scope of improvement. A comparison has also been turned based on their incorporated features and evaluation results. It is expected that this article will draw the attention of more researchers in the area of Bangla text summarization and give a crystal-clear message about the opportunities to the next generation. The integrated message about all the existing methods has been depicted here to reveal the importance of Bangla text summariza...

With the increased advancement in technology and the proliferation of internet applications, electronic mails become an increasingly essential means of communication, for both individuals and organizations. In the recent years, however, spam's become the most unsolicited forms of messages that invade the most important services of internet: Electronic mail and search engines. The last decade witnessed a great reaction towards these annoying spam's by applying a variety of filtering techniques to combat spammers, based on the assumption that in any spam mail, there are specific words/patterns that provide indications of spam's, that is, there are predefined barriers that distinguish a spam message from a legitimate one. The most used techniques for spam detection are Bayesian Classifiers that have been argued to be efficient for filtering E-mail spam. However, as spam detection techniques evolve, spammers evolve too so as to prove their excellence to adapt to the predefin...

CS educators have paid a great deal of attention to Dis-crete Mathematics over the past several decades. Although there have been many suggestions for improving this course, it seems to us that the real purpose of Discrete Mathematics as a transitional course has been long forgotten. We believe that the most important objective of this course is to let students be familiar with the format and structure of rigorous mathematical arguments for their future study in CS. What subjects should be taught are also important because they are tools for us to effectively achieve the objective we just mentioned. Thus, we argue that complicated subjects should not be used in this transitional course because they will distract the student's attention from the underlying structure of the arguments that we want to emphasize. In this paper we start with some prevailing misconceptions in the effort of improving this course, and then we provide our solutions.

Multi – document as one of summarization type has become more challenging issue than single-document because its larger space and its different content of each document. Hence, some of optimization algorithms consider some criteria in producing the best summary, such as relevancy, content coverage, and diversity. Those weighted criteria based on the assumption that the multi-documents are already located in the same cluster. However, in a certain condition, multi-documents consist of many categories and need to be considered too. In this paper, we propose an inter and intra cluster which consist of four weighted criteria functions (coherence, coverage, diversity, and inter-cluster analysis) to be optimized by using SaDE (Self Adaptive Differential Evolution) to get the best summary result. Therefore, the proposed method will deal not only with the value of compactness quality of the cluster within but also the separation of each cluster. Experimental results on Text Analysis Confere...

Due to the exponential growth of textual information available on the Web, end users need to be able to access information in summary form-and without losing the most important information in the document when generating the summaries. Automatic generation of extractive summaries from a single document has traditionally been given the task of extracting the most relevant sentences from the original document. The methods employed generally allocate a score to each sentence in the document, taking into account certain features. The most relevant sentences are then selected, according to the score obtained for each sentence. These features include the position of the sentence in the document, its similarity to the title, the sentence length, and the frequency of the terms in the sentence. However, it has still not been possible to achieve a quality of summary that matches that performed by humans and therefore methods continue to be brought forward that aim to improve on the results. This paper addresses the generation of extractive summaries from a single document as a binary optimization problem where the quality (fitness) of the solutions is based on the weighting of individual statistical features of each sentence-such as position, sentence length and the relationship of the summary to the title, combined with group features of similarity between candidate sentences in the summary and the original document, and among the candidate sentences of the summary. This paper proposes a method of extractive single-document summarization based on genetic operators and guided local search, called MA-Sin-gleDocSum. A memetic algorithm is used to integrate the own-population-based search of evolutionary algorithms with a guided local search strategy. The proposed method was compared with the state of the art methods UnifiedRank, DE, FEOM, NetSum, CRF, QCS, SVM, and Manifold Ranking, using ROUGE measures on the datasets DUC2001 and DUC2002. The results showed that MA-SingleDocSum outperforms the state of the art methods.

The portfolio has long been used as a tool for monitoring student progress. In computer science, the programming portfolio contains a selection of computer programs that a student has produced over a period of time. Usually this has consisted of a notebook containing pages of program listings, perhaps combined with text-based example runs or graphics-based snapshots showing particular moments of program execution. While useful, reviewing such material is about as exciting as watching paint dry and fails to capture the essence of the programs in action. With the advent of the World Wide Web and the Java-based applet, the programming portfolio can be revitalized into an interactive format conducive to both student and reviewer.

Multi-document summarization consists in automatically producing a unique informative summary from a collection of texts on the same topic. In this paper we model the multi-document summarization task as a problem of machine learning classification where sentences from the source texts have to be classified as belonging or not to the summary. For this aim, we combine superficial (e.g., sentence position in the text) and deep linguistic features (e.g. semantic relations across documents). In particular, the linguistic features are given by CST (Cross-document Structure Theory). We conduct our experiments on a CST-annotated corpus of news texts. Results show that linguistic features help to produce a better classification model, producing state-ofthe-art results.

Our ITiCSE 2002 working group 'Materials Development in Support of Mathematical Thinking' identified the development of an on-line repository as the best mechanism for organizing and disseminating materials promoting mathematical thinking in computer science education.

In this study we propose an automatic single document text summarization technique using Latent Semantic Analysis (LSA) and diversity constraint in combination. The proposed technique uses the query based sentence ranking. Here we are not considering the concept of IR (Information Retrieval) so we generate the query by using the TF-IDF(Term Frequency-Inverse Document Frequency). For producing the query vector, we identify the terms having the high IDF. We know that LSA utilizes the vectorial semantics to analyze the relationships between documents in a corpus or between sentences within a document and key terms they carry by producing a list of ideas interconnected to the documents and terms. LSA helps to represent the latent structure of documents. For selecting the sentences from the document Latent Semantic Indexing (LSI) is used. LSI helps to arrange the sentences with its score. Traditionally the highest score sentences have been chosen for summary but here we calculate the div...

The methods of Automatic Extractive Summarization (AES) uses the features of the sentences of the original text to extract the most important information that will be considered in summary. It is known that the first sentences of the text are more relevant than the rest of the text (this heuristic is called baseline), so the position of the sentence (in reverse order) is used to determine its relevance, which means that the last sentences have practically no possibility of being selected. In this paper, we present a way to soften the importance of sentences according to the position. The comprehensive tests were done on one of the best AES methods using the bag of words and n-grams models with the with DUC02 and DUC01 data sets to determine the importance of sentences.

The technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Nowadays, document summarization plays an important role in information retrieval. With a large volume of documents, presenting the user with a summary of each document greatly facilitates the task of finding the desired documents. Document summarization is a process of automatically creating a compressed version of a given document that provides useful information to users, and multi-document summarization is to produce a summary delivering the majority of information content from a set of documents about an explicit or implicit main topic. According to the input text, in this paper we use the knowledge base of Wikipedia and the words of the main text to create independent graphs. We will then determine the important of graphs. Then we are specified importance of graph and sentences that have topics with high importance. Finally, we extract sentences with high importance. The experimental results on an open benchmark datasets from DUC01 and DUC02 show that our proposed approach can improve the performance compared to state-of-the-art summarization approaches.

In extraction-based automatic text summarization (ATS) applications, feature scoring is the cornerstone of the summarization process since it is used for selecting the candidate summary sentences. Handling all features equally leads to generating disqualified summaries. Feature Weighting (FW) is an important approach used to weight the scores of the features based on their presence importance in the current context. Therefore, some of the ATS researchers have proposed evolutionary-based machine learning methods, such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), to extract superior weights to their assigned features. Then the extracted weights are used to tune the scored-features in order to generate a high qualified summary. In this paper, the Differential Evolution (DE) algorithm was proposed to act as a feature weighting machine learning method for extraction-based ATS problems. In addition to enabling the DE to represent and control the assigned features in binary dimension space, it was modulated into a binary coded format. Simple mathematical calculation features have been selected from various literature and employed in this study. The sentences in the documents are first clustered according to a multi-objective clustering concept. DE approach simultaneously optimizes two objective functions, which are compactness measuring and separating the sentence clusters based on these objectives. In order to automatically detect a number of sentence clusters contained in a document, representative sentences from various clusters are chosen with certain sentence scoring features to produce the summary. The method was tested and trained using DUC2002 dataset to learn the weight of each feature. To create comparative and competitive findings, the proposed DE method was compared with evolutionary methods: PSO and GA. The DE was also compared against the best and worst systems benchmark in DUC 2002. The performance of the BiDETS model is scored with 49% similar to human performance (52%) in ROUGE-1; 26% which is over the human performance (23%) using ROUGE-2; and lastly 45% similar to human performance (48%) using ROUGE-L. These results showed that the proposed method outperformed all other methods in terms of F-measure using the ROUGE evaluation tool.

Text summarization is a process for creating a concise version of document(s) preserving its main content. In this paper, to cover all topics and reduce redundancy in summaries, a two-stage sentences selection method for text summarization is proposed. At the first stage, to discover all topics the sentences set is clustered by using k-means method. At the second stage, optimum selection of sentences is proposed. From each cluster the salient sentences are selected according to their contribution to the topic (cluster) and their proximity to other sentences in cluster to avoid redundancy in summaries until the appointed summary length is reached. Sentence selection is modeled as an optimization problem. In this study, to solve the optimization problem an adaptive differential evolution with novel mutation strategy is employed. With a test on benchmark DUC2001 and DUC2002 data sets, the ROUGE value of summaries got by the proposed approach demonstrated its validity, compared to the t...

Due to the exponential growth of textual information available on the Web, end users need to be able to access information in summary form-and without losing the most important information in the document when generating the summaries. Automatic generation of extractive summaries from a single document has traditionally been given the task of extracting the most relevant sentences from the original document. The methods employed generally allocate a score to each sentence in the document, taking into account certain features. The most relevant sentences are then selected, according to the score obtained for each sentence. These features include the position of the sentence in the document, its similarity to the title, the sentence length, and the frequency of the terms in the sentence. However, it has still not been possible to achieve a quality of summary that matches that performed by humans and therefore methods continue to be brought forward that aim to improve on the results. This paper addresses the generation of extractive summaries from a single document as a binary optimization problem where the quality (fitness) of the solutions is based on the weighting of individual statistical features of each sentence-such as position, sentence length and the relationship of the summary to the title, combined with group features of similarity between candidate sentences in the summary and the original document, and among the candidate sentences of the summary. This paper proposes a method of extractive single-document summarization based on genetic operators and guided local search, called MA-Sin-gleDocSum. A memetic algorithm is used to integrate the own-population-based search of evolutionary algorithms with a guided local search strategy. The proposed method was compared with the state of the art methods UnifiedRank, DE, FEOM, NetSum, CRF, QCS, SVM, and Manifold Ranking, using ROUGE measures on the datasets DUC2001 and DUC2002. The results showed that MA-SingleDocSum outperforms the state of the art methods.

We show that frequent patterns can contribute to the quality of text summarization. Here we focus on single-document extractive summarization in English. Performance of the frequent patterns based model obtained with DGRMiner yields the most relevant sentences of all compared methods. Two out of three proposed methods outperform other methods if compared on ROUGE data.

It is very common that students attending urban campuses do not live in university residence halls or dorms. In our case, many of our students spend around one hour and a half each way between home and school. We have developed an App for mobile devices that provides computer programming exercises and quizzes to help students practice and self-assess their knowledge during their commuting time. The most relevant part of this App is that it can be used off-line while the students travel in the subway without an Internet connection. In this paper, we discuss the features of this App and how it has been used to help increase the passing rate, from 65% to 76%, in an introductory computer programming course.

This paper introduces the methods and experiments applied in CIST system participating in the CLSciSumm 2016 Shared Task at BIRNDL 2016. We have participated in the TAC 2014 Biomedical Summarization Track, so we develop the system based on previous work. This time the domain is Computational Linguistics (CL). The training corpus contains 20 topics from Training-Set-2016 and Development-Set-Apr8 published by CL-SciSumm 2016. As to Task 1A and 1B, we mainly use rule-based methods with various features of lexicons and similarities; meanwhile we also have tried the machine learning method of SVM. As to Task 2, hLDA topic model is adopted for content modeling, which provides us knowledge about sentence clustering (subtopic) and word distributions (abstractiveness) for summarization. We then combine hLDA knowledge with several other classical features using different weights and proportions to evaluate the sentences in the Reference Paper from its cited text spans. Finally we extract the ...

We examine the effect of probabilistic topic model-based word representations, on sentence-based extractive summarization. We formulate the task of sentence selection as a binary classification problem, and we test a variety of machine learning algorithms, exploring a range of different settings for classification and modelling. A preliminary investigation via a wide experimental evaluation on the MultiLing 2015 MSS dataset illustrates that topicbased representations can prove beneficial to the extractive summarization process, compared to a TF-IDF baseline, with Quadratic Discriminant Analysis and Gradient Boosting providing the best results for micro and macro F1 score, respectively.

Patents provide inventors exclusive rights to their inventions by protecting their intellectual property rights. However, analyzing patent documents generally requires knowledge of various fields, considerable human labor, and expertise. Recent studies to alleviate this problem on patent analysis deal only with the analysis of claims and abstract parts, neglecting the descriptions that contain essential technical cores. Moreover, few studies use a deep learning approach to handle the entire patent analysis process, including preprocessing, summarization, and key-phrase generation. Therefore, we propose a novel multi-stage framework that can aid in analyzing patent documents by using the description part of the patent rather than abstracts or claims with deep learning. The framework comprises two stages: key-sentence extraction and key-phrase generation tasks. These stages are based on the T5 model structure, transformerbased architecture that uses a text-to-text approach. To further improve the framework's performance, we employed two key factors: i) post-training the model with a patent-related raw corpus for encouraging the model's comprehension of the patent domain, and ii) utilizing a text rank algorithm for efficient training based on the priority score of each sentence. We verified that our key-phrase generation method of the framework shows higher performance in both superficial and semantic evaluation than other extraction methods. In addition, we provided the validity and effectiveness of our methods through quantitative and qualitative analysis, demonstrating the practical functionality of our methods. We also provided a practical contribution to the patent analysis by releasing the framework as a demo system.

The NSF/IEEE-TCPP Parallel and Distributed Computing curriculum guidelines released in 2012 (PDC12) is an effort to bring more parallel computing education to early computer science courses. It has been moderately successful, with the inclusion of some PDC topics in the ACM/IEEE Computer Science curriculum guidelines in 2013 (CS13) and some coverage of topics in early CS courses in some universities in the U.S. and around the world. A reason often cited for the lack of a broader adoption is the difficulty for instructors who are not already knowledgable in PDC topics to learn how to teach those topics and align their learning objectives with early CS courses. There have been attempts at bringing textbook chapters, lecture slides, assignments, and demos to the hands of the instructors of early CS classes. However, the effort required to plow through all the available materials and figure out what is relevant to a particular class is daunting. This paper argues that classifying pedagogical materials against the CS13 guidelines and the PDC12 guidelines can provide the means necessary to reduce the burden of adoption for instructors. In this paper, we present CAR-CS, a system that can be used to categorize pedagogical materials according to wellknown and established curricular guidelines and show that CAR-CS can be leveraged 1) by PDC experts to identify topics for which pedagogical material does not exist and that should be developed, 2) by instructors of early CS courses to find materials that are similar to the one that they use but that also cover PDC topics, 3) by instructors to check the topics that a course currently covers and those it does not cover.

Computing proficiency is an increasingly vital component of the modern workforce, and computer science programs are faced with the challenges of engaging and retaining students to meet the growing need in that sector. However, administrators and instructors often find themselves either reinventing the wheel or relying too heavily on intuition, despite the availability of national curriculum standards. To address these issues, we present CS Materials, an open-source resource targeted at computing educators for designing and analyzing courses for coverage of recommended guidelines, and alignment between the various components within a course, between sections of the same course, or course sequences within a program. The system works by facilitating mapping educational materials to national curriculum standards. A side effect of the system is that it centralizes the design of the courses and the materials used therein. The curriculum guidelines act as a lingua franca that allows examination of and comparison between materials and courses. More relevant to instructors, the system enables a more precise search for materials that match particular topics and learning outcomes, and dissemination of high quality materials and course designs. This paper discusses the system, and analyzes the costs and benefits of its features and usage. While adding courses and materials requires some overhead, having a centralized repository of courses and materials with a shared structure and vocabulary serves students, instructors, and administrators, by promoting a data-driven approach to rigor and alignment with national standards.

This paper describes our experience in creating and teaching an introductory, undergraduate Game Development course for non-Computer Science majors. The hands-on course uses the Unity game engine to teach a variety of Computer Science (CS) topics and inspire students to consider majoring in Computer Science. The paper discusses the course objectives, the approach to presenting the material, and the course outcomes in terms of student success and CS majors recruitment.

Given the rapid growth in online information content, text summarization algorithms are progressively used to provide users a succinct idea about the total information content. Historically, summarization algorithms are evaluated only based on how close their outputs are to human-written gold standard summaries. In this work, we propose to evaluate summarization algorithms from an unprecedented viewpoint. Considering that an extractive summarization algorithm selects a subset of the textual units (tweets / sentences) in the input data for inclusion in the summary, we examine the fairness of this selection. Importantly, if the data to be summarized is generated by different socially salient groups, or different political groups, or different news media sources, then we check whether the generated summaries fairly represent these different groups or sources. In real-world datasets we observe that existing summarization algorithms often represent the groups very differently compared to...

Nowadays, the amount of Arabic documents has increased significantly in different domains, such as news articles, emails, business summary, biomedicine, web sites and social media documents. Some databases have increased in its size to terabyte. Multi-document summarization is the method of creating a summary of a group of interrelated documents. Therefore, the rise of the desire for Arabic multi documents text summarization (at the instant rates possible, coherent, grammatical and meaningful sentences) is increased. Recently, many efforts on multi-document text summarization that is related to the English language have been performed. Arabic multi-document summarization is remained on its early stages. Consequently, the researchers in this paper propose an Arabic Multi-Document Text Summarization (AMD-TS) model based on parallel computing techniques. This model of Arabic text summarization could effectively and rapidly summarize Arabic multi-documents in real time. A conceptual framework is proposed based on published researches dealing with text summarization techniques of different languages. The proposed model creates an accurate, coherent and complete Arabic multi-document text summarization model. The dataset that is used in the investigation stage is derived from different domains, such as education, sports and politics. This dataset contains texts of various sizes. The experiments are then designed to be on specific domain (news articles domain). In order to increase the summarization process efficiency and performance, the researchers in this paper use parallel computing. The model covers the deficiency of Arabic Automatic Summarization Systems (ASS) by enhancing the final summary.

As the volume of online and electronic information increasingly has grown, quickly and accurately access to these important resources is a big challenge. Text analytics can help by transposing words and sentences in unstructured data into high-quality information. Text summarization is one of the applications of text mining, has been of interest to researchers. In addition to text summarization, using optimization algorithms can be influenced results. In this paper, has been presented a hybrid approach for English multi-document summarization. As name suggest, a text summarization system produces summary of original documents. Combination of text mining and optimization algorithms is main ways this research, to improve results and reduce redundancy in summary sentences and simultaneously summary sentences have the most relevant. Similarity measures are cosine and overlap. Using multi-objective particle swarm optimization algorithm improved results. The experimental results of the method on two data sets DUC2005 and DUC2007 show improvements in the three assessment criteria associated. Result of summarization about 3 percent in ROUGE-1, in ROUGE-2 at 2 percent and the benchmark ROUGE-SU for approximately 1.5% compared with the previous methods