Exploiting relevance, coverage, and novelty for query-focused multi-document summarization

Wenjuan Luo; Fuzhen Zhuang; Qing He; Zhongzhi Shi

doi:10.1016/j.knosys.2013.02.015

ScienceGate Book Chapters

JOURNAL ARTICLE

Exploiting relevance, coverage, and novelty for query-focused multi-document summarization

Wenjuan Luo Fuzhen Zhuang Qing He Zhongzhi Shi

Year: 2013 Journal: Knowledge-Based Systems Vol: 46 Pages: 33-42 Publisher: Elsevier BV

DOI: 10.1016/j.knosys.2013.02.015

Get Full-Text PDF Get Analytical Report

Keywords:

Automatic summarization Relevance (law) Computer science Novelty Information retrieval Multi-document summarization Exploit Topic model Ranking (information retrieval) Probabilistic logic Data mining Artificial intelligence

Metrics

Cited By

3.30

FWCI (Field Weighted Citation Impact)

Refs

0.94

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Topic Modeling

Physical Sciences → Computer Science → Artificial Intelligence

Natural Language Processing Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Information Retrieval and Search Behavior

Physical Sciences → Computer Science → Information Systems

Exploiting relevance, coverage, and novelty for query-focused multi-document summarization

Metrics

Citation History

Topics

Related Documents

Exploiting novelty, coverage and balance for topic-focused multi-document summarization

Query-Focused Multi-document Summarization

Query-Focused Multi-document Summarization Survey

Measuring importance and query relevance in topic-focused multi-document summarization

Diffusion Language Model with Query-Document Relevance for Query-Focused Summarization