{"task":"Text Summarization","dataset":"DUC 2004 Task 1","metric_names":["ROUGE-1","ROUGE-2","ROUGE-L"],"rows":[{"id":54779,"task":"Text Summarization","parent_task":null,"dataset":"DUC 2004 Task 1","model_name":"Transformer+WDrop","metrics":{"ROUGE-1":"33.06","ROUGE-2":"11.45","ROUGE-L":"28.51"},"paper_url":"https://arxiv.org/abs/2104.01853v1","paper_title":"Rethinking Perturbations in Encoder-Decoders for Fast Training","paper_date":"2021-04-05","code_links":[{"title":"takase/rethink_perturbations","url":"https://github.com/takase/rethink_perturbations"}],"metrics_order":"[\"ROUGE-1\", \"ROUGE-2\", \"ROUGE-L\"]","area":"Knowledge Base","uses_additional_data":1,"source":"archive","tags":[]},{"id":54780,"task":"Text Summarization","parent_task":null,"dataset":"DUC 2004 Task 1","model_name":"Transformer+LRPE+PE+Re-ranking+Ensemble","metrics":{"ROUGE-1":"32.85","ROUGE-2":"11.78","ROUGE-L":"28.52"},"paper_url":"http://arxiv.org/abs/1904.07418v1","paper_title":"Positional Encoding to Control Output Sequence Length","paper_date":"2019-04-16","code_links":[{"title":"takase/control-length","url":"https://github.com/takase/control-length"}],"metrics_order":"[\"ROUGE-1\", \"ROUGE-2\", \"ROUGE-L\"]","area":"Knowledge Base","uses_additional_data":0,"source":"archive","tags":[]},{"id":54781,"task":"Text Summarization","parent_task":null,"dataset":"DUC 2004 Task 1","model_name":"Transformer+LRPE+PE+ALONE+Re-ranking","metrics":{"ROUGE-1":"32.57","ROUGE-2":"11.63","ROUGE-L":"28.24"},"paper_url":"https://arxiv.org/abs/2004.12073v3","paper_title":"All Word Embeddings from One Embedding","paper_date":"2020-04-25","code_links":[{"title":"takase/alone_seq2seq","url":"https://github.com/takase/alone_seq2seq"}],"metrics_order":"[\"ROUGE-1\", \"ROUGE-2\", \"ROUGE-L\"]","area":"Knowledge Base","uses_additional_data":0,"source":"archive","tags":[]},{"id":54782,"task":"Text Summarization","parent_task":null,"dataset":"DUC 2004 Task 1","model_name":"EndDec+WFE","metrics":{"ROUGE-1":"32.28","ROUGE-2":"10.54","ROUGE-L":"27.8"},"paper_url":"http://arxiv.org/abs/1701.00138v2","paper_title":"Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization","paper_date":"2016-12-31","code_links":[],"metrics_order":"[\"ROUGE-1\", \"ROUGE-2\", \"ROUGE-L\"]","area":"Knowledge Base","uses_additional_data":0,"source":"archive","tags":[]},{"id":54783,"task":"Text Summarization","parent_task":null,"dataset":"DUC 2004 Task 1","model_name":"DRGD","metrics":{"ROUGE-1":"31.79","ROUGE-2":"10.75","ROUGE-L":"27.48"},"paper_url":"http://arxiv.org/abs/1708.00625v1","paper_title":"Deep Recurrent Generative Decoder for Abstractive Text Summarization","paper_date":"2017-08-02","code_links":[{"title":"toru34/li_emnlp_2017","url":"https://github.com/toru34/li_emnlp_2017"}],"metrics_order":"[\"ROUGE-1\", \"ROUGE-2\", \"ROUGE-L\"]","area":"Knowledge Base","uses_additional_data":0,"source":"archive","tags":[]},{"id":54784,"task":"Text Summarization","parent_task":null,"dataset":"DUC 2004 Task 1","model_name":"Reinforced-Topic-ConvS2S","metrics":{"ROUGE-1":"31.15","ROUGE-2":"10.85","ROUGE-L":"27.68"},"paper_url":"https://arxiv.org/abs/1805.03616v3","paper_title":"A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization","paper_date":"2018-05-09","code_links":[],"metrics_order":"[\"ROUGE-1\", \"ROUGE-2\", \"ROUGE-L\"]","area":"Knowledge Base","uses_additional_data":0,"source":"archive","tags":[]},{"id":54785,"task":"Text Summarization","parent_task":null,"dataset":"DUC 2004 Task 1","model_name":"Seq2seq + selective + MTL + ERAM","metrics":{"ROUGE-1":"29.33","ROUGE-2":"10.24","ROUGE-L":"25.24"},"paper_url":"https://aclanthology.org/C18-1121","paper_title":"Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization","paper_date":"2018-08-01","code_links":[],"metrics_order":"[\"ROUGE-1\", \"ROUGE-2\", \"ROUGE-L\"]","area":"Knowledge Base","uses_additional_data":0,"source":"archive","tags":[]},{"id":54786,"task":"Text Summarization","parent_task":null,"dataset":"DUC 2004 Task 1","model_name":"SEASS","metrics":{"ROUGE-1":"29.21","ROUGE-2":"9.56","ROUGE-L":"25.51"},"paper_url":"http://arxiv.org/abs/1704.07073v1","paper_title":"Selective Encoding for Abstractive Sentence Summarization","paper_date":"2017-04-24","code_links":[{"title":"magic282/SEASS","url":"https://github.com/magic282/SEASS"},{"title":"toru34/zhou_acl_2017","url":"https://github.com/toru34/zhou_acl_2017"}],"metrics_order":"[\"ROUGE-1\", \"ROUGE-2\", \"ROUGE-L\"]","area":"Knowledge Base","uses_additional_data":0,"source":"archive","tags":[]},{"id":54787,"task":"Text Summarization","parent_task":null,"dataset":"DUC 2004 Task 1","model_name":"RAS-Elman","metrics":{"ROUGE-1":"28.97","ROUGE-2":"8.26","ROUGE-L":"24.06"},"paper_url":"https://aclanthology.org/N16-1012","paper_title":"Abstractive Sentence Summarization with Attentive Recurrent Neural Networks","paper_date":"2016-06-01","code_links":[],"metrics_order":"[\"ROUGE-1\", \"ROUGE-2\", \"ROUGE-L\"]","area":"Knowledge Base","uses_additional_data":0,"source":"archive","tags":[]},{"id":54788,"task":"Text Summarization","parent_task":null,"dataset":"DUC 2004 Task 1","model_name":"words-lvt5k-1sent","metrics":{"ROUGE-1":"28.61","ROUGE-2":"9.42","ROUGE-L":"25.24"},"paper_url":"http://arxiv.org/abs/1602.06023v5","paper_title":"Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond","paper_date":"2016-02-19","code_links":[{"title":"theamrzaki/text_summurization_abstractive_methods","url":"https://github.com/theamrzaki/text_summurization_abstractive_methods"},{"title":"kashgupta/textsummarization","url":"https://github.com/kashgupta/textsummarization"},{"title":"yunzhusong/AAAI20-PORLHG","url":"https://github.com/yunzhusong/AAAI20-PORLHG"},{"title":"meghu2791/DeepLearningModels","url":"https://github.com/meghu2791/DeepLearningModels"}],"metrics_order":"[\"ROUGE-1\", \"ROUGE-2\", \"ROUGE-L\"]","area":"Knowledge Base","uses_additional_data":0,"source":"archive","tags":[]},{"id":54789,"task":"Text Summarization","parent_task":null,"dataset":"DUC 2004 Task 1","model_name":"Abs+","metrics":{"ROUGE-1":"28.18","ROUGE-2":"8.49","ROUGE-L":"23.81"},"paper_url":"http://arxiv.org/abs/1509.00685v2","paper_title":"A Neural Attention Model for Abstractive Sentence Summarization","paper_date":"2015-09-02","code_links":[{"title":"tensorflow/models","url":"https://github.com/tensorflow/models/tree/master/research/textsum"},{"title":"toru34/rush_emnlp_2015","url":"https://github.com/toru34/rush_emnlp_2015"},{"title":"Ganeshpadmanaban/Neural-Attention-Model-Abstractive-Summarization","url":"https://github.com/Ganeshpadmanaban/Neural-Attention-Model-Abstractive-Summarization"},{"title":"Ganeshpadmanaban/Neural-Attention-Model","url":"https://github.com/Ganeshpadmanaban/Neural-Attention-Model"}],"metrics_order":"[\"ROUGE-1\", \"ROUGE-2\", \"ROUGE-L\"]","area":"Knowledge Base","uses_additional_data":0,"source":"archive","tags":[]},{"id":54790,"task":"Text Summarization","parent_task":null,"dataset":"DUC 2004 Task 1","model_name":"Transformer LM","metrics":{"ROUGE-2":"17.74"},"paper_url":"https://arxiv.org/abs/1905.08836v1","paper_title":"Sample Efficient Text Summarization Using a Single Pre-Trained Transformer","paper_date":"2019-05-21","code_links":[{"title":"tensorflow/tensor2tensor","url":"https://github.com/tensorflow/tensor2tensor"},{"title":"t080/pytorch-translm","url":"https://github.com/t080/pytorch-translm"}],"metrics_order":"[\"ROUGE-1\", \"ROUGE-2\", \"ROUGE-L\"]","area":"Knowledge Base","uses_additional_data":0,"source":"archive","tags":[]},{"id":54791,"task":"Text Summarization","parent_task":null,"dataset":"DUC 2004 Task 1","model_name":"ABS","metrics":{"ROUGE-L":"22.05"},"paper_url":"http://arxiv.org/abs/1509.00685v2","paper_title":"A Neural Attention Model for Abstractive Sentence Summarization","paper_date":"2015-09-02","code_links":[{"title":"tensorflow/models","url":"https://github.com/tensorflow/models/tree/master/research/textsum"},{"title":"toru34/rush_emnlp_2015","url":"https://github.com/toru34/rush_emnlp_2015"},{"title":"Ganeshpadmanaban/Neural-Attention-Model-Abstractive-Summarization","url":"https://github.com/Ganeshpadmanaban/Neural-Attention-Model-Abstractive-Summarization"},{"title":"Ganeshpadmanaban/Neural-Attention-Model","url":"https://github.com/Ganeshpadmanaban/Neural-Attention-Model"}],"metrics_order":"[\"ROUGE-1\", \"ROUGE-2\", \"ROUGE-L\"]","area":"Knowledge Base","uses_additional_data":0,"source":"archive","tags":[]}]}