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We use a novel sentence-level policy gradient method to bridge\nthe non-differentiable computation between these two neural networks in a\nhierarchical way, while maintaining language fluency. Empirically, we achieve\nthe new state-of-the-art on all metrics (including human evaluation) on the\nCNN/Daily Mail dataset, as well as significantly higher abstractiveness scores.\nMoreover, by first operating at the sentence-level and then the word-level, we\nenable parallel decoding of our neural generative model that results in\nsubstantially faster (10-20x) inference speed as well as 4x faster training\nconvergence than previous long-paragraph encoder-decoder models. We also\ndemonstrate the generalization of our model on the test-only DUC-2002 dataset,\nwhere we achieve higher scores than a state-of-the-art model.","url_abs":"http://arxiv.org/abs/1805.11080v1","url_pdf":"http://arxiv.org/pdf/1805.11080v1.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":"fast-abstractive-summarization-with-reinforce","repo_url":"https://github.com/ChenRocks/fast_abs_rl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"fast-abstractive-summarization-with-reinforce","repo_url":"https://github.com/johnnyb1509/2020_VBDI_DL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"fast-abstractive-summarization-with-reinforce","repo_url":"https://github.com/yunzhusong/AAAI20-PORLHG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"abstractive-text-summarization","task_name":"Abstractive Text Summarization"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-rewriting","task_name":"Sentence ReWriting"},{"task_slug":"text-summarization","task_name":"Text Summarization"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/abstractive-text-summarization-on-cnn-daily","task":"Abstractive Text Summarization","dataset":"CNN / Daily Mail","model":"rnn-ext + RL","rank_in_archive_order":33,"of":53,"metrics":{"ROUGE-1":"41.47","ROUGE-2":"18.72","ROUGE-L":"37.76"},"uses_additional_data":false},{"leaderboard":"/sota/abstractive-text-summarization-on-cnn-daily","task":"Abstractive Text Summarization","dataset":"CNN / Daily Mail","model":"rnn-ext + abs + RL + rerank","rank_in_archive_order":38,"of":53,"metrics":{"ROUGE-1":"40.88","ROUGE-2":"17.80","ROUGE-L":"38.54"},"uses_additional_data":false},{"leaderboard":"/sota/text-summarization-on-cnn-daily-mail-2","task":"Text Summarization","dataset":"CNN / Daily Mail (Anonymized)","model":"rnn-ext + abs + RL + rerank","rank_in_archive_order":7,"of":13,"metrics":{"ROUGE-1":"39.66","ROUGE-2":"15.85","ROUGE-L":"37.34"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.11080","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.11080"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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