Summarization of Yes/No Questions Using a Feature Function Model


Jing He, Decheng Dai ;
Proceedings of the Asian Conference on Machine Learning, PMLR 20:351-366, 2011.


Answer summarization is an important problem in the study of Question and Answering. In this paper, we deal with the general questions with “Yes/No” answers in English. We design 1) a model to score the relevance of the answers and the questions, and 2) a feature function combining the relevance and opinion scores to classify each answer to be “Yes”, “No” or “Neutral”. We combine the opinion features together with two weighting scores to solve this problem and conduct experiments on a real word dataset. Given an input question, the system firstly detects if it can be simply answered by “Yes/No” or not, and then outputs the resulting voting numbers of “Yes” answers and “No” answers to this question. We also first proposed the accuracy, precision, and recall to the “Yes/No” answer detection.

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