NDLI: Improving Summarization Quality with Topic Modeling

Please wait, while we are loading the content...

Making Topic Models more Usable

Community Topic Usage in Social Networks

Supervised Dynamic Topic Models for Associative Topic Extraction with A Numerical Time Series

Combining Topic Models for Corpus Exploration: Applying LDA for Complex Corpus Research Tasks in a Digital Humanities Project

Semantic Topic-based Hybrid Learning Resource Recommendation

Topic Models Regularization and Initialization for Regression Problems

Getting the Agenda Right: Measuring Media Agenda using Topic Models

Non-Bayesian Additive Regularization for Multimodal Topic Modeling of Large Collections

Improving Summarization Quality with Topic Modeling

Improving Summarization Quality with Topic Modeling

Content Provider	ACM Digital Library
Author	Savas, Onur Liu, Chunlei Litvak, Marina Vanetik, Natalia Xiao, Lemin
Abstract	The problem of extractive text summarization for a collection of documents is defined as the problem of selecting a small subset of sentences so that the contents and meaning of the original document set are preserved in the best possible way. In this paper we describe different applications of topic modeling as it relates to summarization. We consider two summarization models - supervised and unsupervised - enhanced by integrating the topic knowledge into their standard lexicon-based models. Both summarizers strive to cover as much information of the input documents as possible when generating the summaries. The supervised summarizer operates in a standard rank-and-select-sentences manner of extractive summarization, where the best linear combination of multiple sentence features is learned by a genetic algorithm (GA). The unsupervised summarizer models the summarization task as an optimization problem. As is the case with most existing summarization approaches, both original models measure information coverage by lexical units and were enriched by topic knowledge, providing a new measure for the informaition coverage. The experimental results show that utilizing topic knowledge improves the summarization quality.
Starting Page	39
Ending Page	47
Page Count	9
File Format	PDF
ISBN	9781450337847
DOI	10.1145/2809936.2809944
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-10-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Topic modeling Maximal coverage Polytope model Genetic algorithm Lda Linear programming Optimization Summarization
Content Type	Text
Resource Type	Article

Central Library (ISO-9001:2015 Certified)
Indian Institute of Technology Kharagpur
Kharagpur, West Bengal, India | PIN - 721302

See location in the Map
03222 282435
Mail: support@ndl.gov.in

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in