Towards content-level coherence with aspect-guided summarization

Renxian Zhang, Wenjie Li, Dehong Gao

Research output: Journal article publicationReview articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

The TAC 2010 summarization track initiated a new task - aspect-guided summarization - that centers on textual aspects embodied as particular kinds of information of a text. We observe that aspect-guided summaries not only address highly specific user need, but also facilitate content-level coherence by using aspect information. In this article, we present a full-fledged approach to aspect-guided summarization with a focus on summary coherence. Our summarization approach depends on two prerequisite subtasks: recognizing aspect-bearing sentences in order to do sentence extraction, and modeling aspect-based coherence with an HMM model in order to predict a coherent sentence ordering. Using the manually annotated TAC 2010 and 2010 datasets, we validated the effectiveness of our proposed methods for those subtasks. Drawing on the empirical results, we proceed to develop an aspect-guided summarizer based on a simple but robust base summarizer. With sentence selection guided by aspect information, our system is one of the best on TAC 2011. With sentence ordering predicted by the aspect-based HMM model, the summaries achieve good coherence.
Original languageEnglish
Article number2
JournalACM Transactions on Speech and Language Processing
Volume10
Issue number1
DOIs
Publication statusPublished - 1 Mar 2013

Keywords

  • Algorithms
  • Experimentation

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computational Mathematics

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