A Discourse-Driven Content Model for Summarising Scientific Articles Evaluated in a Complex Question Answering Task

We present a method which exploits automatically generated scientific discourse annotations to create a content model for the summarisation of scientific articles. Full papers are first automatically annotated using the CoreSC scheme, which captures 11 contentbased concepts such as Hypothesis, Result, Conclusion etc at the sentence level. A content model which follows the sequence of CoreSC categories observed in abstracts is used to provide the skeleton of the summary, making a distinction between dependent and independent categories. Summary creation is also guided by the distribution of CoreSC categories found in the full articles, in order to adequately represent the article content. Finally, we demonstrate the usefulness of the summaries by evaluating them in a complex question answering task. Results are very encouraging as summaries of papers from automatically obtained CoreSCs enable experts to answer 66% of complex content-related questions designed on the basis of paper abstracts. The questions were answered with a precision of 75%, where the upper bound for human summaries (abstracts) was 95%.

[1]  Maria Liakata,et al.  The ART Corpus , 2009 .

[2]  Kathleen McKeown,et al.  Text generation: using discourse strategies and focus constraints to generate natural language text , 1985 .

[3]  Anna Korhonen,et al.  Using Argumentative Zones for Extractive Summarization of Scientific Articles , 2012, COLING.

[4]  David R. Karger,et al.  Content Modeling Using Latent Permutations , 2009, J. Artif. Intell. Res..

[5]  Lisa F. Rau,et al.  Automatic Condensation of Electronic Publications by Sentence Selection , 1995, Inf. Process. Manag..

[6]  Sampo Pyysalo,et al.  Overview of BioNLP’09 Shared Task on Event Extraction , 2009, BioNLP@HLT-NAACL.

[7]  Simone Teufel,et al.  Towards Domain-Independent Argumentative Zoning: Evidence from Chemistry and Computational Linguistics , 2009, EMNLP.

[8]  Junichi Tsujii,et al.  Event extraction for systems biology by text mining the literature. , 2010, Trends in biotechnology.

[9]  Regina Barzilay,et al.  Catching the Drift: Probabilistic Content Models, with Applications to Generation and Summarization , 2004, NAACL.

[10]  Dietrich Rebholz-Schuhmann,et al.  Automatic recognition of conceptualization zones in scientific articles and two life science applications , 2012, Bioinform..

[11]  Task-Based Evaluation of Summary Quality: Describing Relationships between Scientific Papers , 2001 .

[12]  Elizabeth Du,et al.  The discourse-level structure of empirical abstracts: an exploratory study , 1991, Inf. Process. Manag..

[13]  Rada Mihalcea,et al.  TextRank: Bringing Order into Text , 2004, EMNLP.

[14]  Dragomir R. Radev,et al.  Citation Summarization Through Keyphrase Extraction , 2010, COLING.

[15]  Simone Teufel,et al.  The Structure of Scientific Articles - Applications to Citation Indexing and Summarization , 2010, CSLI Studies in Computational Linguistics.

[16]  M. Rey.  Improving summarization through rhetorical parsing tuning , 1998 .

[17]  Ani Nenkova,et al.  Automatically Assessing Machine Summary Content Without a Gold Standard , 2013, CL.

[18]  Dragomir R. Radev,et al.  Scientific Paper Summarization Using Citation Summary Networks , 2008, COLING.

[19]  Simone Teufel.  Towards Discipline-Independent Argumentative Zoning : Evidence from Chemistry and Computational Linguistics , 2009 .

[20]  Regina Barzilay,et al.  Incorporating Content Structure into Text Analysis Applications , 2010, EMNLP.

[21]  William C. Mann,et al.  Rhetorical Structure Theory: Toward a functional theory of text organization , 1988 .

[22]  Dragomir R. Radev,et al.  LexRank: Graph-based Lexical Centrality as Salience in Text Summarization , 2004, J. Artif. Intell. Res..

[23]  Francine Chen,et al.  A trainable document summarizer , 1995, SIGIR '95.

[24]  Alexander Gelbukh,et al.  Comparing Commercial Tools and State-of-the-Art Methods for Generating Text Summaries , 2009, 2009 Eighth Mexican International Conference on Artificial Intelligence.

[25]  K. Bretonnel Cohen,et al.  Hypothesis and Evidence Extraction from Full-Text Scientific Journal Articles , 2011, BioNLP@ACL.

[26]  Thierry Poibeau,et al.  A Weakly-supervised Approach to Argumentative Zoning of Scientific Documents , 2011, EMNLP.

[27]  Daniel Marcu,et al.  The rhetorical parsing, summarization, and generation of natural language texts , 1998 .

[28]  Dragomir R. Radev,et al.  Identifying Non-Explicit Citing Sentences for Citation-Based Summarization. , 2010, ACL.

[29]  William R. Hersh,et al.  A Survey of Current Work in Biomedical Text Mining , 2005 .

[30]  Simone Teufel,et al.  Corpora for the Conceptualisation and Zoning of Scientific Papers , 2010, LREC.

[31]  Marc Moens,et al.  Articles Summarizing Scientific Articles: Experiments with Relevance and Rhetorical Status , 2002, CL.

[32]  Ani Nenkova,et al.  A Coherence Model Based on Syntactic Patterns , 2012, EMNLP.