Syntactic simplification for improving content selection in multi-document summarization

72Citations
Citations of this article
143Readers
Mendeley users who have this article in their library.

Abstract

In this paper, we explore the use of automatic syntactic simplification for improving content selection in multi-document summarization. In particular, we show how simplifying parentheticals by removing relative clauses and appositives results in improved sentence clustering, by forcing clustering based on central rather than background information. We argue that the inclusion of parenthetical information in a summary is a reference-generation task rather than a content-selection one, and implement a baseline reference rewriting module. We perform our evaluations on the test sets from the 2003 and 2004 Document Understanding Conference and report that simplifying parentheticals results in significant improvement on the automated evaluation metric Rouge.

References Powered by Scopus

Rhetorical Structure Theory: Toward a functional theory of text organization

2539Citations
N/AReaders
Get full text

Disambiguation of proper names in text

96Citations
N/AReaders
Get full text

Aggregation in natural language generation

28Citations
N/AReaders
Get full text

Cited by Powered by Scopus

A survey of text summarization techniques

456Citations
N/AReaders
Get full text

Automatic summarization

379Citations
N/AReaders
Get full text

Machine learning for text

207Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Siddharthan, A., Nenkova, A., & McKeown, K. (2004). Syntactic simplification for improving content selection in multi-document summarization. In COLING 2004 - Proceedings of the 20th International Conference on Computational Linguistics. Association for Computational Linguistics (ACL). https://doi.org/10.3115/1220355.1220484

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 60

67%

Researcher 15

17%

Professor / Associate Prof. 9

10%

Lecturer / Post doc 5

6%

Readers' Discipline

Tooltip

Computer Science 72

77%

Linguistics 13

14%

Engineering 7

7%

Neuroscience 2

2%

Save time finding and organizing research with Mendeley

Sign up for free