{"url":"/sota/text-summarization-on-cnn-daily-mail-2","task":{"name":"Text Summarization","url":"/task/text-summarization","note":null},"dataset":{"name":"CNN / Daily Mail (Anonymized)","url":"/dataset/cnn-daily-mail-1"},"category":"Natural Language Processing","categories":["Knowledge Base","Natural Language Processing"],"category_note":null,"description":"**Text Summarization** is a natural language processing (NLP) task that involves condensing a lengthy text document into a shorter, more compact version while still retaining the most important information and meaning. The goal is to produce a summary that accurately represents the content of the original text in a concise form. \r\n\r\nThere are different approaches to text summarization, including extractive methods that identify and extract important sentences or phrases from the text, and abstractive methods that generate new text based on the content of the original text.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["ROUGE-1","ROUGE-2","ROUGE-L"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"ROUGE-1":"higher","ROUGE-2":"higher","ROUGE-L":"higher"}},"counts":{"rows":13,"rows_with_code":8,"rows_with_paper_page":13,"rows_dated":13,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"HSSAS","metrics":{"ROUGE-1":"42.3","ROUGE-2":"17.8","ROUGE-L":"37.6"},"uses_additional_data":false,"paper_date":"2018-05-20","paper":"/paper/a-hierarchical-structured-self-attentive","paper_url":"http://arxiv.org/abs/1805.07799v1","paper_title":"A Hierarchical Structured Self-Attentive Model for Extractive Document Summarization (HSSAS)","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":2,"model":"SWAP-NET","metrics":{"ROUGE-1":"41.6","ROUGE-2":"18.3","ROUGE-L":"37.7"},"uses_additional_data":false,"paper_date":"2018-07-01","paper":"/paper/extractive-summarization-with-swap-net","paper_url":"https://aclanthology.org/P18-1014","paper_title":"Extractive Summarization with SWAP-NET: Sentences and Words from Alternating Pointer Networks","code":"https://github.com/aishj10/swap-net","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"RNES w/o coherence","metrics":{"ROUGE-1":"41.25","ROUGE-2":"18.87","ROUGE-L":"37.75"},"uses_additional_data":false,"paper_date":"2018-04-19","paper":"/paper/learning-to-extract-coherent-summary-via-deep","paper_url":"http://arxiv.org/abs/1804.07036v1","paper_title":"Learning to Extract Coherent Summary via Deep Reinforcement Learning","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":4,"model":"ML+RL ROUGE+Novel, with LM","metrics":{"ROUGE-1":"40.02","ROUGE-2":"15.53","ROUGE-L":"37.44"},"uses_additional_data":false,"paper_date":"2018-08-23","paper":"/paper/improving-abstraction-in-text-summarization","paper_url":"http://arxiv.org/abs/1808.07913v1","paper_title":"Improving Abstraction in Text Summarization","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":5,"model":"GAN","metrics":{"ROUGE-1":"39.92","ROUGE-2":"17.65","ROUGE-L":"36.71"},"uses_additional_data":false,"paper_date":"2017-11-26","paper":"/paper/generative-adversarial-network-for","paper_url":"http://arxiv.org/abs/1711.09357v1","paper_title":"Generative Adversarial Network for Abstractive Text Summarization","code":"https://github.com/iwangjian/textsum-gan","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"ML+RL, with intra-attention","metrics":{"ROUGE-1":"39.87","ROUGE-2":"15.82","ROUGE-L":"36.90"},"uses_additional_data":false,"paper_date":"2017-05-11","paper":"/paper/a-deep-reinforced-model-for-abstractive","paper_url":"http://arxiv.org/abs/1705.04304v3","paper_title":"A Deep Reinforced Model for Abstractive Summarization","code":"https://github.com/theamrzaki/text_summurization_abstractive_methods","n_code_links":10,"syntology":null},{"rank_in_archive_order":7,"model":"rnn-ext + abs + RL + rerank","metrics":{"ROUGE-1":"39.66","ROUGE-2":"15.85","ROUGE-L":"37.34"},"uses_additional_data":false,"paper_date":"2018-05-28","paper":"/paper/fast-abstractive-summarization-with-reinforce","paper_url":"http://arxiv.org/abs/1805.11080v1","paper_title":"Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting","code":"https://github.com/ChenRocks/fast_abs_rl","n_code_links":3,"syntology":{"n_ran":3,"n_unverified":0,"n_samples":3,"n_pointer_only_licence":2}},{"rank_in_archive_order":8,"model":"SummaRuNNer","metrics":{"ROUGE-1":"39.6","ROUGE-2":"16.2","ROUGE-L":"35.3"},"uses_additional_data":false,"paper_date":"2016-11-14","paper":"/paper/summarunner-a-recurrent-neural-network-based","paper_url":"http://arxiv.org/abs/1611.04230v1","paper_title":"SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents","code":"https://github.com/kedz/nnsum","n_code_links":7,"syntology":{"n_ran":0,"n_unverified":7,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":9,"model":"Lead-3 baseline","metrics":{"ROUGE-1":"39.2","ROUGE-2":"15.7","ROUGE-L":"35.5"},"uses_additional_data":false,"paper_date":"2016-11-14","paper":"/paper/summarunner-a-recurrent-neural-network-based","paper_url":"http://arxiv.org/abs/1611.04230v1","paper_title":"SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents","code":"https://github.com/kedz/nnsum","n_code_links":7,"syntology":{"n_ran":0,"n_unverified":7,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":10,"model":"KIGN+Prediction-guide","metrics":{"ROUGE-1":"38.95","ROUGE-2":"17.12","ROUGE-L":"35.68"},"uses_additional_data":false,"paper_date":"2018-06-01","paper":"/paper/guiding-generation-for-abstractive-text","paper_url":"https://aclanthology.org/N18-2009","paper_title":"Guiding Generation for Abstractive Text Summarization Based on Key Information Guide Network","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":11,"model":"Fastformer","metrics":{"ROUGE-1":"38.54","ROUGE-2":"16.22","ROUGE-L":"36.21"},"uses_additional_data":false,"paper_date":"2021-08-20","paper":"/paper/fastformer-additive-attention-is-all-you-need","paper_url":"https://arxiv.org/abs/2108.09084v6","paper_title":"Fastformer: Additive Attention Can Be All You Need","code":"https://github.com/keonlee9420/Comprehensive-Transformer-TTS","n_code_links":13,"syntology":{"n_ran":4,"n_unverified":0,"n_samples":4,"n_pointer_only_licence":3}},{"rank_in_archive_order":12,"model":"Tan et al.","metrics":{"ROUGE-1":"38.1","ROUGE-2":"13.9","ROUGE-L":"34.0"},"uses_additional_data":false,"paper_date":"2017-07-01","paper":"/paper/abstractive-document-summarization-with-a","paper_url":"https://aclanthology.org/P17-1108","paper_title":"Abstractive Document Summarization with a Graph-Based Attentional Neural Model","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":13,"model":"words-lvt2k-temp-att","metrics":{"ROUGE-1":"35.46","ROUGE-2":"13.30","ROUGE-L":"32.65"},"uses_additional_data":false,"paper_date":"2016-02-19","paper":"/paper/abstractive-text-summarization-using-sequence","paper_url":"http://arxiv.org/abs/1602.06023v5","paper_title":"Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond","code":"https://github.com/theamrzaki/text_summurization_abstractive_methods","n_code_links":4,"syntology":{"n_ran":5,"n_unverified":1,"n_samples":6,"n_pointer_only_licence":0}}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,264 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6264,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":3316},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":5,"rows_with_any_sample_ran":3,"distinct_papers_with_graph_line":4,"distinct_papers_with_any_sample_ran":3,"samples_over_distinct_papers":{"n_ran":12,"n_unverified":8,"n_samples":20,"n_pointer_only_licence":5,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":12,"n_unverified":15,"n_samples":27,"n_pointer_only_licence":5,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}