research-article

ELSA: A Multilingual Document Summarization Algorithm Based on Frequent Itemsets and Latent Semantic Analysis

Authors:

Elena BaralisAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 37, Issue 2

Article No.: 21, Pages 1 - 33

https://doi.org/10.1145/3298987

Published: 16 January 2019 Publication History

Abstract

Sentence-based summarization aims at extracting concise summaries of collections of textual documents. Summaries consist of a worthwhile subset of document sentences. The most effective multilingual strategies rely on Latent Semantic Analysis (LSA) and on frequent itemset mining, respectively. LSA-based summarizers pick the document sentences that cover the most important concepts. Concepts are modeled as combinations of single-document terms and are derived from a term-by-sentence matrix by exploiting Singular Value Decomposition (SVD). Itemset-based summarizers pick the sentences that contain the largest number of frequent itemsets, which represent combinations of frequently co-occurring terms. The main drawbacks of existing approaches are (i) the inability of LSA to consider the correlation between combinations of multiple-document terms and the underlying concepts, (ii) the inherent redundancy of frequent itemsets because similar itemsets may be related to the same concept, and (iii) the inability of itemset-based summarizers to correlate itemsets with the underlying document concepts. To overcome the issues of both of the abovementioned algorithms, we propose a new summarization approach that exploits frequent itemsets to describe all of the latent concepts covered by the documents under analysis and LSA to reduce the potentially redundant set of itemsets to a compact set of uncorrelated concepts. The summarizer selects the sentences that cover the latent concepts with minimal redundancy. We tested the summarization algorithm on both multilingual and English-language benchmark document collections. The proposed approach performed significantly better than both itemset- and LSA-based summarizers, and better than most of the other state-of-the-art approaches.

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Index Terms

ELSA: A Multilingual Document Summarization Algorithm Based on Frequent Itemsets and Latent Semantic Analysis
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 37, Issue 2

April 2019

410 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3306215

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University of Amsterdam, The Netherlands

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Publication History

Published: 16 January 2019

Accepted: 01 November 2018

Revised: 01 November 2018

Received: 01 February 2018

Published in TOIS Volume 37, Issue 2

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https://doi.org/10.31796/ogummf.1303569
Umair MBouguettaya ALakhdari AOuzzani MLiu Y(2024)Exif2Vec: A Framework to Ascertain Untrustworthy Crowdsourced Images Using MetadataACM Transactions on the Web10.1145/364509418:3(1-27)Online publication date: 13-Feb-2024
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