{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/summareranker-a-multi-task-mixture-of-experts-1","title":"SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization","arxiv_id":"2203.06569","date":"2022-03-13","proceeding":"ACL 2022 5","authors":["Mathieu Ravaut","Shafiq Joty","Nancy F. Chen"],"abstract":"Sequence-to-sequence neural networks have recently achieved great success in abstractive summarization, especially through fine-tuning large pre-trained language models on the downstream dataset. These models are typically decoded with beam search to generate a unique summary. However, the search space is very large, and with the exposure bias, such decoding is not optimal. In this paper, we show that it is possible to directly train a second-stage model performing re-ranking on a set of summary candidates. Our mixture-of-experts SummaReranker learns to select a better candidate and consistently improves the performance of the base model. With a base PEGASUS, we push ROUGE scores by 5.44% on CNN-DailyMail (47.16 ROUGE-1), 1.31% on XSum (48.12 ROUGE-1) and 9.34% on Reddit TIFU (29.83 ROUGE-1), reaching a new state-of-the-art. Our code and checkpoints will be available at https://github.com/ntunlp/SummaReranker.","url_abs":"https://arxiv.org/abs/2203.06569v2","url_pdf":"https://arxiv.org/pdf/2203.06569v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"summareranker-a-multi-task-mixture-of-experts-1","repo_url":"https://github.com/ntunlp/summareranker","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"abstractive-text-summarization","task_name":"Abstractive Text Summarization"},{"task_slug":"document-summarization","task_name":"Document Summarization"},{"task_slug":"mixture-of-experts","task_name":"Mixture-of-Experts"},{"task_slug":"re-ranking","task_name":"Re-Ranking"},{"task_slug":"text-summarization","task_name":"Text Summarization"}],"methods":[{"method_slug":"base","method_name":"BASE"},{"method_slug":"pegasus","method_name":"PEGASUS"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/abstractive-text-summarization-on-cnn-daily","task":"Abstractive Text Summarization","dataset":"CNN / Daily Mail","model":"PEGASUS + SummaReranker","rank_in_archive_order":4,"of":53,"metrics":{"ROUGE-1":"47.16","ROUGE-2":"22.61","ROUGE-L":"43.87"},"uses_additional_data":false},{"leaderboard":"/sota/document-summarization-on-cnn-daily-mail","task":"Document Summarization","dataset":"CNN / Daily Mail","model":"PEGASUS + SummaReranker","rank_in_archive_order":2,"of":26,"metrics":{"ROUGE-1":"47.16","ROUGE-2":"22.55","ROUGE-L":"43.87"},"uses_additional_data":false},{"leaderboard":"/sota/text-summarization-on-reddit-tifu","task":"Text Summarization","dataset":"Reddit TIFU","model":"PEGASUS + SummaReranker","rank_in_archive_order":4,"of":5,"metrics":{"ROUGE-1":"29.83","ROUGE-2":"9.5","ROUGE-L":"23.47"},"uses_additional_data":false},{"leaderboard":"/sota/text-summarization-on-x-sum","task":"Text Summarization","dataset":"X-Sum","model":"PEGASUS + SummaReranker","rank_in_archive_order":3,"of":18,"metrics":{"ROUGE-1":"48.12","ROUGE-2":"24.95","ROUGE-L":"40.00"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2203.06569","atlas_url":"https://app.syntology.ai/?focus=2203.06569","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.06569"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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