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 4: Evaluation on Text Summarization
['[BOLD] Models', '[BOLD] ROUGE 1', '[BOLD] ROUGE 2', '[BOLD] ROUGE L']
The experimental results show that the question-driven PGN outperforms all the state-of-the-art methods of both extractive and abstractive summarization, which demonstrates the effectiveness of incorporating question information to generate summaries for answers. The question information directly involves in the calculation of the generation probability to determine the next word whether generated from the vocabulary or copied from the source text. In addition, jointly learning with answer selection, ASAS further improves the result with a noticeable margin. The correlation information between question-answer pairs also aids in attending important words in the original answer, which are related to the question. These results show that ASAS can effectively generate high-quality summaries for the selected answers.