Text Summarization using QA Corpus for User Interaction Model QA System
Автор: K.Karpagam, A. Saradha, K.Manikandan, K.Madusudanan
Журнал: International Journal of Education and Management Engineering @ijeme
Статья в выпуске: 3 vol.10, 2020 года.
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Document summarization is capable of generating user query relevant, precise summaries from the original document for user needs. To reduce the response time summary generation, QA corpus is built for similar questions and answer with help of learning model. It has been trained and tested by Quora duplicate and Yahoo! Answer datasets. The large QA corpus has been dynamically clustered with semantic features paves a way for efficient document’s retrieval. Answers are produced from datasets or generate summaries for unanswerable from the available sources. Results obtained from statistical significance test with hypothesis testing and evaluation with standard metrics proves the significant improvement in generating text summarization using QA corpus. The outcome is better in the producing close proximity of answers for the given user query.
Question Answering System, QA corpus, Text summarization, Machine Learning
Короткий адрес: https://sciup.org/15017256
IDR: 15017256 | DOI: 10.5815/ijeme.2020.03.04
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