A Study on Social Context Summarization
| Project/Area Number |
15K16048
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Intelligent informatics
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| Research Institution | Japan Advanced Institute of Science and Technology |
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Principal Investigator |
Nguyen Minh Le 北陸先端科学技術大学院大学, 先端科学技術研究科, 准教授 (30509401)
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2017: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2015: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | social context / extractive / abstractive / deep learning / seq2seq model / learning to rank / LSTM / LSTM-CRF / Sentence extraction / Social context / Deep Learning / Sentence compression / Co-factorization / comment extraction / sentence extraction / text summarization / contextual summarization / word representation |
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| Outline of Final Research Achievements |
In this project, we aim at studying social context summarization. We study the effect of user comments to summarization system and how we can summary comments using the content of the document. (1) We consider the problem of extractive summarization using social context. (2) The problem of sentence compression and abstractive text summarization are studied. The social context summarization is formulated as selecting importance information on the graph.
We created the data for our research on social context summarization. The experimental results of the proposed system showed that social context information is useful for text summarization.
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Report
(4 results)
Research Products
(23 results)
