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SoRTESum: A Social Context Framework for Single-Document Summarization Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Multi-document Summarization Based on Atomic Semantic Events and Their Temporal Relationships

Authors : Yllias Chali, Mohsin Uddin

Published in: Advances in Information Retrieval

Publisher: Springer International Publishing

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Abstract

Automatic multi-document summarization (MDS) is the process of extracting the most important information, such as events and entities, from multiple natural language texts focused on the same topic. In this paper, we experiment with the effects of different groups of information such as events and named entities in the domain of generic and update MDS. Our generic MDS system has outperformed the best recent generic MDS systems in DUC 2004 in terms of ROUGE-1 recall and \(f_1\)-measure. Update summarization is a new form of MDS, where novel yet salient sentences are chosen as summary sentences based on the assumption that the user has already read a given set of documents. We present an event based update summarization where the novelty is detected based on the temporal ordering of events, and the saliency is ensured by the event and entity distribution. To our knowledge, no other study has deeply experimented with the effects of the novelty information acquired from the temporal ordering of events (assuming that a sentence contains one or more events) in the domain of update multi-document summarization. Our update MDS system has outperformed the state-of-the-art update MDS system in terms of ROUGE-2 and ROUGE-SU4 recall measures. All our MDS systems also generate quality summaries which are manually evaluated based on popular evaluation criteria.

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previous chapter Evaluating Text Summarization Systems with a Fair Baseline from Multiple Reference Summaries
next chapter Tweet Stream Summarization for Online Reputation Management
Footnotes
1
Here ‘22 years’ is a time period. Time periods do not carry important information for detecting novelty.
 
5
Document Creation Time (DCT) can be calculated from document name.
 
6
Total 4 topics are taken into account, i.e. K is 4.
 
7
ROUGE runtime arguments for DUC 2004:
\(ROUGE \text{- }a \text{- }c 95 \text{- }b 665 \text{- }m \text{- }n 4 \text{- }w 1.2\).
 
8
We do not compare our system with the recent topic model based system [14] because that system is significantly outperformed by Lin and Bilmes’s [23] system in terms of both ROUGE-1 recall and \(f_1\)-measure.
 
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Metadata
Title
Multi-document Summarization Based on Atomic Semantic Events and Their Temporal Relationships
Authors
Yllias Chali
Mohsin Uddin
Copyright Year
2016
Publisher
Springer International Publishing
Book
Advances in Information Retrieval

Print ISBN: 978-3-319-30670-4

Electronic ISBN: 978-3-319-30671-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-30671-1_27