research-article

Towards content-level coherence with aspect-guided summarization

Authors:

Dehong GaoAuthors Info & Claims

ACM Transactions on Speech and Language Processing (TSLP), Volume 10, Issue 1

Article No.: 2, Pages 1 - 22

https://doi.org/10.1145/2442076.2442078

Published: 22 March 2013 Publication History

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.

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Cited By

Chen XXie HTao XXu LWang JDai HWang F(2024)A topic modeling‐based bibliometric exploration of automatic summarization researchWIREs Data Mining and Knowledge Discovery10.1002/widm.1540Online publication date: 25-Apr-2024
https://doi.org/10.1002/widm.1540
Narayan SGardent C(2020)Deep Learning Approaches to Text ProductionSynthesis Lectures on Human Language Technologies10.2200/S00979ED1V01Y201912HLT04413:1(1-199)Online publication date: 20-Mar-2020
https://doi.org/10.2200/S00979ED1V01Y201912HLT044
Yao JWan XXiao J(2017)Recent advances in document summarizationKnowledge and Information Systems10.1007/s10115-017-1042-453:2(297-336)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1007/s10115-017-1042-4

Index Terms

Towards content-level coherence with aspect-guided summarization
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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cover image ACM Transactions on Speech and Language Processing

ACM Transactions on Speech and Language Processing Volume 10, Issue 1

March 2013

50 pages

ISSN:1550-4875

EISSN:1550-4883

DOI:10.1145/2442076

Issue’s Table of Contents

Copyright © 2013 ACM.

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Association for Computing Machinery

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Publication History

Published: 22 March 2013

Accepted: 01 January 2013

Revised: 01 January 2013

Received: 01 February 2012

Published in TSLP Volume 10, Issue 1

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Cited By

Chen XXie HTao XXu LWang JDai HWang F(2024)A topic modeling‐based bibliometric exploration of automatic summarization researchWIREs Data Mining and Knowledge Discovery10.1002/widm.1540Online publication date: 25-Apr-2024
https://doi.org/10.1002/widm.1540
Narayan SGardent C(2020)Deep Learning Approaches to Text ProductionSynthesis Lectures on Human Language Technologies10.2200/S00979ED1V01Y201912HLT04413:1(1-199)Online publication date: 20-Mar-2020
https://doi.org/10.2200/S00979ED1V01Y201912HLT044
Yao JWan XXiao J(2017)Recent advances in document summarizationKnowledge and Information Systems10.1007/s10115-017-1042-453:2(297-336)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1007/s10115-017-1042-4

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