Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering

Understand

Community question answering (CQA) gains increasing popularity in both academy and industry recently.

• However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users.
• To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model.
• Specifically, we design a question-driven pointer-generator network, which exploits the correlation information between question-answer pairs to aid in attending the essential information when generating answer summaries.