Unsupervised Sentence Embeddings for Answer Summarization in Non-factoid CQA

Thi-Thanh Ha, Thanh-Chinh Nguyen, Kiem-Hieu Nguyen, Van-Chung Vu, Kim-Anh Nguyen


This paper presents a method for summarizing answers in Community Question Answering.We explore deep Auto-encoder and Long-short-termmemory 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.


Summarizing answers, non-factoid questions, multi-documment summarization, community question-answering, auto encoder, LSTM

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