Answer summarization for technical queries: Benchmark and new approach

• Main Authors: YANG, Chengran, XU, Bowen, THUNG, Ferdian, SHI, Yucen, ZHANG, Ting, YANG, Zhou, ZHOU, Xin, SHI, Jieke, HE, Junda, HAN, DongGyun, David LO
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Summarization; Question retrieval; Pre-trained models; Artificial Intelligence and Robotics; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7714; https://ink.library.smu.edu.sg/context/sis_research/article/8717/viewcontent/2209.10868.pdf
• Institution: Singapore Management University

Prior studies have demonstrated that approaches to generate an answer summary for a given technical query in Software Question and Answer (SQA) sites are desired. We find that existing approaches are assessed solely through user studies. Hence, a new user study needs to be performed every time a new...