Joint Learning of Answer Selection and Answer Summary Generation in Community Question AnsweringThis work was financially supported by the National Natural Science Foundation of China (No.61602013) and a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: 14204418).
Table 5: Human Evaluation Results
['Method', 'Info', 'Conc', 'Read', 'Corr']
The results show that ASAS consistently outperforms other methods in all aspects. Noticeably, the proposed method learns well to generate answer summaries that are highly related to the given questions so there is a substantial margin on Correlatedness. In order to intuitively observe the advantage of the proposed method, we randomly choose one example to show the answer summary generation results. As shown in the Fig. (e.g., NeuralSum) selects important sentences from the original answer to form the answer summary, which still contains many insignificant or redundant information. The abstractive method (e.g., PGN) generates the answer summary from the vocabulary and the original answer, which may miss some key words and essential information. Upon these defects, the proposed joint learning method (ASAS) takes into account the information provided by the question to capture the core idea of the original answer and generate a precise summary. More importantly, unlike other methods, answer summaries are generated at the same time that the answers are selected.