A Novel Ilp Framework For Summarizing Content With High Lexical Variety

Abstract

Summarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events. However, there remains a significant need to summarize such content. Examples include the student responses to post-class reflective questions, product reviews, and news articles published by different news agencies related to the same events. High lexical diversity of these documents hinders the system's ability to effectively identify salient content and reduce summary redundancy. In this paper, we overcome this issue by introducing an integer linear programming-based summarization framework. It incorporates a low-rank approximation to the sentence-word cooccurrence matrix to intrinsically group semantically similar lexical items. We conduct extensive experiments on datasets of student responses, product reviews, and news documents. Our approach compares favorably to a number of extractive baselines as well as a neural abstractive summarization system. The paper finally sheds light on when and why the proposed framework is effective at summarizing content with high lexical variety.

Publication Date

11-1-2018

Publication Title

Natural Language Engineering

Volume

24

Issue

6

Number of Pages

887-920

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1017/S1351324918000323

Socpus ID

85053198699 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85053198699

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