{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:07:11Z","timestamp":1764850031503,"version":"3.46.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686387","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,2]]},"abstract":"<jats:p>Court decision summarization is challenging due to the significant length and structural complexity of legal documents, which makes existing reinforcement learning (RL)-based abstractive summarization methods less effective. We propose an RL-based abstractive legal summarization model with a novel argument-structured reward mechanism. It leverages Issue\u2013Reason\u2013Conclusion components to compute fine-grained sub-rewards and aggregate them into a final reward. This design provides more reliable learning signals for model optimization. Experiments on the Indian Supreme Court dataset with Longformer-Encoder-Decoder (LED) and Llama demonstrate consistent improvements over non-argument-structured methods. To the best of our knowledge, this is the first work to incorporate argumentative structures into RL-based summarization, offering a novel direction for improving legal summarization.<\/jats:p>","DOI":"10.3233\/faia251603","type":"book-chapter","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:05:16Z","timestamp":1764849916000},"source":"Crossref","is-referenced-by-count":0,"title":["Reinforcement Learning with Argument-Structured Reward for Court Decision Abstractive Summarization"],"prefix":"10.3233","author":[{"given":"Yuntao","family":"Kong","sequence":"first","affiliation":[{"name":"Center of Juris-Informatics, ROIS-DS, Japan"}]},{"given":"Ye","family":"Xiong","sequence":"additional","affiliation":[{"name":"Institute of Science Tokyo, Japan"}]},{"given":"Shuyuan","family":"Zheng","sequence":"additional","affiliation":[{"name":"The University of Osaka, Japan"}]},{"given":"Ken","family":"Satoh","sequence":"additional","affiliation":[{"name":"Center of Juris-Informatics, ROIS-DS, Japan"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Legal Knowledge and Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251603","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:05:17Z","timestamp":1764849917000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251603"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,2]]},"ISBN":["9781643686387"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251603","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,2]]}}}