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A summarization system for Chinese news from multiple sources

Author

Listed:
  • Hsin‐Hsi Chen
  • June‐Jei Kuo
  • Sheng‐Jie Huang
  • Chuan‐Jie Lin
  • Hung‐Chia Wung

Abstract

This article proposes a summarization system for multiple documents. It employs not only named entities and other signatures to cluster news from different sources, but also employs punctuation marks, linking elements, and topic chains to identify the meaningful units (MUs). Using nouns and verbs to identify the similar MUs, focusing and browsing models are applied to represent the summarization results. To reduce information loss during summarization, informative words in a document are introduced. For the evaluation, a question answering system (QA system) is proposed to substitute the human assessors. In large‐scale experiments containing 140 questions to 17,877 documents, the results show that those models using informative words outperform pure heuristic voting‐only strategy by news reporters. This model can be easily further applied to summarize multilingual news from multiple sources.

Suggested Citation

  • Hsin‐Hsi Chen & June‐Jei Kuo & Sheng‐Jie Huang & Chuan‐Jie Lin & Hung‐Chia Wung, 2003. "A summarization system for Chinese news from multiple sources," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(13), pages 1224-1236, November.
    • Handle: RePEc:bla:jamist:v:54:y:2003:i:13:p:1224-1236
    DOI: 10.1002/asi.10315
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    Cited by:

    1. Chandrakala Arya & Manoj Diwakar & Prabhishek Singh & Vijendra Singh & Seifedine Kadry & Jungeun Kim, 2023. "Multi-Document News Web Page Summarization Using Content Extraction and Lexical Chain Based Key Phrase Extraction," Mathematics, MDPI, vol. 11(8), pages 1-20, April.

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