JOURNAL ARTICLE

Abstractive Multi-Document Summarization Based on Semantic Link Network

Wei LiHai Zhuge

Year: 2019 Journal:   IEEE Transactions on Knowledge and Data Engineering Vol: 33 (1)Pages: 43-54   Publisher: IEEE Computer Society

Abstract

The key to realize advanced document summarization is semantic representation of documents. This paper investigates the role of Semantic Link Network in representing and understanding documents for multi-document summarization. It proposes a novel abstractive multi-document summarization framework by first transforming documents into a Semantic Link Network of concepts and events and then transforming the Semantic Link Network into the summary of the documents based on the selection of important concepts and events while keeping semantics coherence. Experiments on benchmark datasets show that the proposed summarization approach significantly outperforms relevant state-of-the-art baselines and the Semantic Link Network plays an important role in representing and understanding documents.

Keywords:
Computer science Automatic summarization Information retrieval Multi-document summarization Link (geometry) Natural language processing Semantics (computer science) Artificial intelligence World Wide Web Programming language Computer network

Metrics

41
Cited By
3.23
FWCI (Field Weighted Citation Impact)
95
Refs
0.93
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Text Analysis Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence
Biomedical Text Mining and Ontologies
Life Sciences →  Biochemistry, Genetics and Molecular Biology →  Molecular Biology

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