Overview of the CL-SciSumm 2016 Shared Task

2020

Related work sections or literature reviews are an essential part of every scientific article being crucial for paper reviewing and assessment. The automatic generation of related work sections can be considered an instance of the multi-document summarization problem. In order to allow the study of this specific problem, we have developed a manually annotated, machine readable data-set of related work sections, cited papers (e.g. references) and sentences, together with an additional layer of papers citing the references. We additionally present experiments on the identification of cited sentences, using as input citation contexts. The corpus alongside the gold standard are made available for use by the scientific community.

Proceedings of the AAAI Conference on Artificial Intelligence

Scientific article summarization is challenging: large, annotated corpora are not available, and the summary should ideally include the article’s impacts on research community. This paper provides novel solutions to these two challenges. We 1) develop and release the first large-scale manually-annotated corpus for scientific papers (on computational linguistics) by enabling faster annotation, and 2) propose summarization methods that integrate the authors’ original highlights (abstract) and the article’s actual impacts on the community (citations), to create comprehensive, hybrid summaries. We conduct experiments to demonstrate the efficacy of our corpus in training data-driven models for scientific paper summarization and the advantage of our hybrid summaries over abstracts and traditional citation-based summaries. Our large annotated corpus and hybrid methods provide a new framework for scientific paper summarization research.

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.

2017

This paper describes our approach on ”Recognizing Reference Spans,Classifying Their Discourse Facets and Summarizing from Reference Text” as an attempt in the shared task on relationship mining and scientific summarization of computational linguistics research papers at SIGIR 2017.

Computational Linguistics, 2002

In this article we propose a strategy for the summarization of scientific articles that concentrates on the rhetorical status of statements in an article: Material for summaries is selected in such a way that summaries can highlight the new contribution of the source article and situate it with respect to earlier work.We provide a gold standard for summaries of this kind consisting of a substantial corpus of conference articles in computational linguistics annotated with human judgments of the rhetorical status and relevance of each sentence in the articles. We present several experiments measuring our judges' agreement on these annotations.We also present an algorithm that, on the basis of the annotated training material, selects content from unseen articles and classifies it into a fixed set of seven rhetorical categories. The output of this extraction and classification system can be viewed as a single-document summary in its own right; alternatively, it provides starting mat...

2020 IEEE International Conference on Big Data (Big Data)

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 extractive evaluation metrics and performs better for abstractive summarization on five human evaluation metrics (entailment, coherence, conciseness, readability, and grammar). We achieve a kappa score of 0.68 between two co-author linguists who evaluated our results. We plan to publicly share MAG-20, a human-validated gold standard dataset of topic-clustered research articles and their summaries to promote research in abstractive summarization.

2019

In this paper we describe our participation to the Task1 of the CL-SciSumm 2019. The task is on automatic paper summarization in the research area of Computational Linguistics. Our approach is a two step binary sentence pair classification between the so-called citances and candidate sentences. Firstly, we classify sentences in the abstracts to predefined classes we call "zones". These zones capture the discourse structure of a scientific publication. We then expand these zones with additional, similar sentences which are found in the main sections of the publication body. We train a Siamese bi-directional GRU neural network with a logistic regression layer to decide if a citance alludes to a candidate sentence. The cited sentences are also assigned one or more discourse facets (i.e., categories defined in the Task) using a multi-class SVM. We ran extensive experiments in three different datasets achieving promising results.

Computational Linguistics, 2012

Discourse models have received significant attention in the computational linguistics community with some important connections to the non-computational discourse community. More recently, the importance of discourse annotation has increased as models generated with supervised machine learning techniques are being used to annotate text automatically. A primary area for annotation is science. The theme of Teufel's book is an important contribution in these areas: discourse models, annotation schemes, and applications. The book is a substantial work, approximately 450 pages of text and appendices. It extends Teufel's Ph.D. thesis with a decade of new work and updated references. The book is content-rich and meticulously written. In addition to presenting Teufel's discourse model, it also works as a good entry point into discourse models and annotation. Because each chapter is structured with background, new material, and a summary, each chapter can be read somewhat independently. Cross-references to other parts of the book are carefully included where warranted. This structure lends itself to using the book as a reference for each of the subtopics or as an introduction to the subject area as a whole, suitable as a textbook. Chapter 1 sets the stage for the rest of the book. The author sets out her fundamental assumptions and hypotheses. The fundamental assumptions arise from three observations that she has made regarding the literature. Scientific discourse contains descriptions of positive and negative states, contains references to others' contributions, and is the result of a rhetorical game intended to promote one's contribution. Chapter 2, on information retrieval and citation indexes, and Chapter 3, on summarization, provide the motivation for the main theme of the book: These two information-based endeavors can be enhanced with automated tools that incorporate an understanding of the rhetorical aspects of science writing. Whereas Chapters 2 and 3 give an overview of current methodologies, Chapter 4, "New Types of Information Access," introduces two new techniques, rhetorical extracts and citation maps, that are suggested as information navigation methods enhanced by knowledge of the discourse that contains the information being accessed. Rhetorical extracts are snippets that can be tailored to user expertise and navigation task. Citation maps are interactive citation indexes that have their citation links augmented with rhetorical or sentiment information. Chapter 5 gives a detailed description of the five scientific text corpora that are used in the research described throughout the book: computational linguistics,

2016

This work is (partly) supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502), the TUNER project (TIN2015-65308-C5-5-R, MINECO/FEDER, UE) and the European Project Dr. Inventor (FP7-ICT-2013.8.1 - Grant no: 611383).

International Journal on Digital Libraries, 2017

Your article is protected by copyright and all rights are held exclusively by Springer-Verlag GmbH Germany. This e-offprint is for personal use only and shall not be selfarchived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com".