Unsupervised Sentence Embeddings for Answer Summarization in Non-factoid CQA
Abstract: This paper presents a method for summarizing answers in Community Question Answering. We explore deep Auto-encoder and Long-short-term-memory Auto-encoder for sentence representation. The sentence representations are used to measure similarity in Maximal Marginal Relevance algorithm for extractive summarization. Experimental results on a benchmark dataset show that our unsupervised method achieves state-of-the-art performance while requiring no annotated data.
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| Main Authors: | , , , , |
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| Format: | Digital revista |
| Language: | English |
| Published: |
Instituto Politécnico Nacional, Centro de Investigación en Computación
2018
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| Online Access: | http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1405-55462018000300835 |
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| Summary: | Abstract: This paper presents a method for summarizing answers in Community Question Answering. We explore deep Auto-encoder and Long-short-term-memory Auto-encoder for sentence representation. The sentence representations are used to measure similarity in Maximal Marginal Relevance algorithm for extractive summarization. Experimental results on a benchmark dataset show that our unsupervised method achieves state-of-the-art performance while requiring no annotated data. |
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