CIST System for CL-SciSumm 2016 Shared Task

2017

The number of publications is rapidly growing and it is essential to enable fast access and analysis of relevant articles. In this paper, we describe a set of methods based on measuring semantic textual similarity, which we use to semantically analyze and summarize publications through other publications that cite them. We report the performance of our approach in the context of the third CL-SciSumm shared task and show that our system performs favorably to competing systems in terms of produced summaries.

2021

Most earlier work on text summarization is carried out on news article datasets. The summary in these datasets is naturally located at the beginning of the text. Hence, a model can spuriously utilize this correlation for summary generation instead of truly learning to summarize. To address this issue, we constructed a new dataset, SUMPUBMED, using scientific articles from the PubMed archive. We conducted a human analysis of summary coverage, redundancy, readability, coherence, and informativeness on SUMPUBMED. SUMPUBMED is challenging because (a) the summary is distributed throughout the text (not-localized on top), and (b) it contains rare domain-specific scientific terms. We observe that seq2seq models that adequately summarize news articles struggle to summarize SUMPUBMED. Thus, SUMPUBMED opens new avenues for the future improvement of models as well as the development of new evaluation metrics.

2020 IEEE International Conference on Big Data (Big Data), 2020

Recent advances in natural language processing have enabled automation of a wide range of tasks, including machine translation, named entity recognition, and sentiment analysis. Automated summarization of documents, or groups of documents, however, has remained elusive, with many efforts limited to extraction of keywords, key phrases, or key sentences. Accurate abstractive summarization has yet to be achieved due to the inherent difficulty of the problem, and limited availability of training data. In this paper, we propose a topic-centric unsupervised multi-document summarization framework to generate extractive and abstractive summaries for groups of scientific articles across 20 Fields of Study (FoS) in Microsoft Academic Graph (MAG) and news articles from DUC-2004 Task 2. The proposed algorithm generates an abstractive summary by developing salient language unit selection and text generation techniques. Our approach matches the state-of-the-art when evaluated on automated extract...

Australasian Medical Journal, 2012

Evidence Based Medicine (EBM) practice requires practitioners to extract evidence from published medical research when answering clinical queries. Due to the time-consuming nature of this practice, there is a strong motivation for systems that can automatically summarise medical documents and help practitioners find relevant information.

2017

This overview describes the official results of the CL-SciSumm Shared Task 2018 -- the first medium-scale shared task on scientific document summarization in the computational linguistics (CL) domain. This year, the dataset comprised 60 annotated sets of citing and reference papers from the open access research papers in the CL domain. The Shared Task was organized as a part of the 41st Annual Conference of the Special Interest Group in Information Retrieval (SIGIR), held in Ann Arbor, USA in July 2018. We compare the participating systems in terms of two evaluation metrics. The annotated dataset and evaluation scripts can be accessed and used by the community from: \url{this https URL}.

DergiPark (Istanbul University), 2023

The rapid development of technology has resulted in a surge in the volume of digital data available. This situation creates a problem for users who need assistance in locating specific information within this massive collection of data, resulting in a time-consuming process. Automatic Text Summarization systems have been developed as a more effective solution than traditional summarization techniques to address this problem and improve user access to relevant information. It is well known that researchers in the health sciences find it difficult to keep up with the latest literature due to their busy schedules. This study aims to produce comprehensive abstracts of Turkish-language scientific papers in the field of health sciences. Although abstracts of scientific papers are already available, more thorough summaries are still needed. To the best of our knowledge, no previous attempt has been made to automatically summarize Turkish language health science papers. For this purpose, a dataset of 105 Turkish papers was collected from DergiPark. Term Frequency, Term Frequency-Inverse Document Frequency, Latent Semantic Analysis, TextRank, and Latent Dirichlet Allocation algorithms were chosen as extractive text summarization methods due to their frequent use in this field. The performance of the text summarization models was evaluated using recall, precision, and F-score metrics, and the algorithms gave satisfactory results for Turkish.

Sustainability

Biomedical text summarization (BTS) is proving to be an emerging area of work and research with the need for sustainable healthcare applications such as evidence-based medicine practice (EBM) and telemedicine which help effectively support healthcare needs of the society. However, with the rapid growth in the biomedical literature and the diversities in its structure and resources, it is becoming challenging to carry out effective text summarization for better insights. The goal of this work is to conduct a comprehensive systematic literature review of significant and high-impact literary work in BTS with a deep understanding of its major artifacts such as databases, semantic similarity measures, and semantic enrichment approaches. In the systematic literature review conducted, we applied search filters to find high-impact literature in the biomedical text summarization domain from IEEE, SCOPUS, Elsevier, EBSCO, and PubMed databases. The systematic literature review (SLR) yielded 81...

2012

Abstract We present an approach for extracting 3-sentence evidence-based summaries relevant to clinical questions. We approach this task as one of query-focused, extractive, single-document summarisation using sentence-specific statistics for each target sentence. We incorporate simple statistics and domain knowledge and show that such an approach is effective for identifying informative sentences from medical abstracts.

Computer Methods and Programs in Biomedicine

Objective: Automatic text summarization tools can help users in the biomedical domain to access information efficiently from a large volume of scientific literature and other sources of text documents. In this paper, we propose a summarization method that combines itemset mining and domain knowledge to construct a concept-based model and to extract the main subtopics from an input document. Our summarizer quantifies the informativeness of each sentence using the support values of itemsets appearing in the sentence. Methods: To address the concept-level analysis of text, our method initially maps the original document to biomedical concepts using the Unified Medical Language System (UMLS). Then, it discovers the essential subtopics of the text using a data mining technique, namely itemset mining, and constructs the summarization model. The employed itemset mining algorithm extracts a set Manuscript 7 April 2017 of frequent itemsets containing correlated and recurrent concepts of the input document. The summarizer selects the most related and informative sentences and generates the final summary. Results: We evaluate the performance of our itemset-based summarizer using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, performing a set of experiments. We compare the proposed method with GraphSum, TexLexAn, SweSum, SUMMA, AutoSummarize, the term-based version of the itemset-based summarizer, and two baselines. The results show that the itemset-based summarizer performs better than the compared methods. The itemset-based summarizer achieves the best scores for all the assessed ROUGE metrics (R-1: 0.7583, R-2: 0.3381, R-W-1.2: 0.0934, and R-SU4: 0.3889). We also perform a set of preliminary experiments to specify the best value for the minimum support threshold used in the itemset mining algorithm. The results demonstrate that the value of this threshold directly affects the accuracy of the summarization model, such that a significant decrease can be observed in the performance of summarization due to assigning extreme thresholds. Conclusion: Compared to the statistical, similarity, and word frequency methods, the proposed method demonstrates that the summarization model obtained from the concept extraction and itemset mining provides the summarizer with an effective metric for measuring the informative content of sentences. This can lead to an improvement in the performance of biomedical literature summarization.

IEEE Access

This work presents a method for summarizing scientific articles from the arXive and PubMed datasets using a greedy Extractive Summarization algorithm. We used the approach along with Variable Neighborhood Search (VNS) to learn what is the top-line exists in the area of Extractive Text Summarization quality in terms of ROUGE scores. The algorithm is based on first selecting for the summary the sentences from the text containing the maximum number of words with the higher TFIDF values along with minimum document frequency parameter tuning for TFIDF vectorization. As a result, the method achieves 0.43/0.12 and 0.40/0.13 for ROUGE-1/ROUGE-2 scores on arXive and PubMed datasets, respectively. These results are comparable to the state-of-the-art models using complex neural network architectures and serious computational resources together with the large amounts of training data. In contrast, our method uses a straightforward statistical inference methodology.