Discovery of Topically Coherent Sentences for Extractive Summarization

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 491–499, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Discovery of Topically Coherent Sentences for Extractive Summarization Asli Celikyilmaz Microsoft Speech Labs Mountain View, CA, 94041 asli@ieee.org Dilek Hakkani-T¨ur Microsoft Speech Labs | Microsoft Research Mountain View, CA, 94041 dilek@ieee.org Abstract Extractive methods for multi-document sum- marization are mainly governed by informa- tion overlap, coherence, and content con- straints. We present an unsupervised proba- bilistic approach to model the hidden abstract concepts across documents as well as the cor- relation between these concepts, to generate topically coherent and non-redundant sum- maries. Based on human evaluations our mod- els generate summaries with higher linguistic quality in terms of coherence, readability, and redundancy compared to benchmark systems. Although our system is unsupervised and opti- mized for topical coherence, we achieve a 44.1 ROUGE on the DUC-07 test set, roughly in the range of state-of-the-art supervised models. 1 Introduction A query-focused multi-document summarization model produces a short-summary text of a set of documents, which are retrieved based on a user’s query. An ideal generated summary text should con- tain the shared relevant content among set of doc- uments only once, plus other unique information from individual documents that are directly related to the user’s query addressing different levels of de- tail. Recent approaches to the summarization task has somewhat focused on the redundancy and co- herence issues. In this paper, we introduce a series of new generative models for multiple-documents, based on a discovery of hierarchical topics and their correlations to extract topically coherent sentences. Prior research has demonstrated the usefulness of sentence extraction for generating summary text taking advantage of surface level features such as word repetition, position in text, cue phrases, etc, (Radev, 2004; Nenkova and Vanderwende, 2005a; Wan and Yang, 2006; Nenkova et al., 2006). Be- cause documents have pre-defined structures (e.g., sections, paragraphs, sentences) for different levels of concepts in a hierarchy, most recent summariza- tion work has focused on structured probabilistic models to represent the corpus concepts (Barzilay et al., 1999; Daum´e-III and Marcu, 2006; Eisenstein and Barzilay, 2008; Tang et al., 2009; Chen et al., 2000; Wang et al., 2009). In particular (Haghighi and Vanderwende, 2009; Celikyilmaz and Hakkani- Tur, 2010) build hierarchical topic models to iden- tify salient sentences that contain abstract concepts rather than specific concepts. Nonetheless, all these systems crucially rely on extracting various levels of generality from documents, focusing little on redun- dancy and coherence issues in model building. A model than can focus on both issues is deemed to be more beneficial for a summarization task. Topical coherence in text involves identifying key concepts, the relationships between these concepts, and linking these relationships into a hierarchy. In this paper, we present a novel, fully generative Bayesian model of document corpus, which can dis- cover topically coherent sentences that contain key shared information with as little detail and redun- dancy as possible. Our model can discover hierar- chical …

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