| ARTICLE |
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| Year : 2012 | Volume
: 58
| Issue : 2 | Page : 155-165 |
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Personalized Document Summarization Using Pseudo Relevance Feedback and Semantic Feature
Sun Park1, Byung Rae Cha2, JangWoo Kwon3
1 Institute Research of Information Science and Engineering Research, Mokpo National University, South Korea
2 SCENT Center, GIST, South Korea
3 Department of Computer & Information Engineering, INHA University, South Korea
Correspondence Address:
Sun Park
Institute Research of Information Science and Engineering Research, Mokpo National University
South Korea
 DOI: 10.4103/0377-2063.96182
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This paper proposes a new automatic personalized document summarization using pseudo relevance feedback (PRF) and semantic features to extract meaningful sentences from retrieval documents in the Internet. The proposed method uses generic summarization based on the semantic features of non-negative matrix factorization to extract sentences that well reflect the major topics of the searched documents. In addition, this method reduces the semantic gap between the low level of summarizing search results and the high level of user's perception; the method uses query-based summarization depending on PRF and semantic features. The method improves the quality of personalized document summarization because the sentences most relevant to the given query are extracted efficiently by using a combination of generic and query-based summarization. The experimental results demonstrate that the proposed method achieves a better document summarization performance than do other methods. |
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