@inproceedings{c1cf94939a5b4afbb2738aab6dd88763,
title = "Towards a Two-Stage Method for Answer Selection and Summarization in Buddhism Community Question Answering",
abstract = "This paper proposes a two-stage learning pipeline for CQA in the Buddhism domain. In the first stage, we trained an answer selection model through Keywords-BERT that performs a deep semantic match for QA pairs. Given a question, our algorithm selects the answer with the highest relatedness score. Stage two also employs the trained Keywords-BERT model to eliminate redundant information and only keep the most relevant sentences of an answer for summary extraction. Our method only requires standard QA pairs for training, significantly reducing the annotation cost and the knowledge threshold for annotators. We tested our model on a self-created Buddhism CQA dataset. Results show that the proposed pipeline outperforms state-of-the-art methods like BERT-Sum in terms of summary quality and model robustness.",
author = "Jiangnan Du and Jun Chen and Suhong Wang and Jianfeng Li and Zhifeng Xiao",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 1st CAAI International Conference on Artificial Intelligence, CICAI 2021 ; Conference date: 05-06-2021 Through 06-06-2021",
year = "2021",
doi = "10.1007/978-3-030-93049-3_21",
language = "English (US)",
isbn = "9783030930486",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "251--260",
editor = "Lu Fang and Yiran Chen and Guangtao Zhai and Jane Wang and Ruiping Wang and Weisheng Dong",
booktitle = "Artificial Intelligence - 1st CAAI International Conference, CICAI 2021, Proceedings",
address = "Germany",
}