{"list":"other","total":98,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"1910.10683","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","created":"2019-10-23","cited_by_count":14599},{"id":"2005.11401","title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks","created":"2020-05-22","cited_by_count":9297},{"id":"1908.10084","title":"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks","created":"2019-08-27","cited_by_count":9170},{"id":"1910.13461","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","created":"2019-10-29","cited_by_count":5893},{"id":"1904.09675","title":"BERTScore: Evaluating Text Generation with BERT","created":"2019-04-21","cited_by_count":5512},{"id":"2004.05150","title":"Longformer: The Long-Document Transformer","created":"2020-04-10","cited_by_count":3413},{"id":"2104.08718","title":"CLIPScore: A Reference-free Evaluation Metric for Image Captioning","created":"2021-04-18","cited_by_count":2424},{"id":"2007.14062","title":"Big Bird: Transformers for Longer Sequences","created":"2020-07-28","cited_by_count":1684},{"id":"1903.10676","title":"SciBERT: A Pretrained Language Model for Scientific Text","created":"2019-03-26","cited_by_count":1414},{"id":"1808.08745","title":"Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization","created":"2018-08-27","cited_by_count":1317},{"id":"1704.04368","title":"Get To The Point: Summarization with Pointer-Generator Networks","created":"2017-04-14","cited_by_count":1292},{"id":"2012.15723","title":"Making Pre-trained Language Models Better Few-shot Learners","created":"2020-12-31","cited_by_count":1256},{"id":"1912.08777","title":"PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization","created":"2019-12-18","cited_by_count":1102},{"id":"2005.00661","title":"On Faithfulness and Factuality in Abstractive Summarization","created":"2020-05-02","cited_by_count":900},{"id":"1602.06023","title":"Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond","created":"2016-02-19","cited_by_count":812},{"id":"2009.06732","title":"Efficient Transformers: A Survey","created":"2020-09-14","cited_by_count":790},{"id":"2011.04006","title":"Long Range Arena: A Benchmark for Efficient Transformers","created":"2020-11-08","cited_by_count":700},{"id":"1910.12840","title":"Evaluating the Factual Consistency of Abstractive Text Summarization","created":"2019-10-28","cited_by_count":584},{"id":"2009.02252","title":"KILT: a Benchmark for Knowledge Intensive Language Tasks","created":"2020-09-04","cited_by_count":573},{"id":"1908.08345","title":"Text Summarization with Pretrained Encoders","created":"2019-08-22","cited_by_count":556},{"id":"1804.05685","title":"A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents","created":"2018-04-16","cited_by_count":478},{"id":"1909.02622","title":"MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance","created":"2019-09-05","cited_by_count":418},{"id":"1509.00685","title":"A Neural Attention Model for Abstractive Sentence Summarization","created":"2015-09-02","cited_by_count":396},{"id":"2004.04228","title":"Asking and Answering Questions to Evaluate the Factual Consistency of Summaries","created":"2020-04-08","cited_by_count":378},{"id":"1705.04304","title":"A Deep Reinforced Model for Abstractive Summarization","created":"2017-05-11","cited_by_count":365},{"id":"2104.05938","title":"QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization","created":"2021-04-13","cited_by_count":365},{"id":"2104.02112","title":"Efficient Attentions for Long Document Summarization","created":"2021-04-05","cited_by_count":345},{"id":"1801.10198","title":"Generating Wikipedia by Summarizing Long Sequences","created":"2018-01-30","cited_by_count":335},{"id":"1804.11283","title":"Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies","created":"2018-04-30","cited_by_count":294},{"id":"2005.03754","title":"FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization","created":"2020-05-07","cited_by_count":293},{"id":"2104.13346","title":"Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics","created":"2021-04-27","cited_by_count":287},{"id":"1810.09305","title":"WikiHow: A Large Scale Text Summarization Dataset","created":"2018-10-18","cited_by_count":204},{"id":"1908.08960","title":"Neural Text Summarization: A Critical Evaluation","created":"2019-08-23","cited_by_count":189},{"id":"2105.08209","title":"BookSum: A Collection of Datasets for Long-form Narrative Summarization","created":"2021-05-18","cited_by_count":184},{"id":"1808.10792","title":"Bottom-Up Abstractive Summarization","created":"2018-08-31","cited_by_count":177},{"id":"1912.13283","title":"oLMpics -- On what Language Model Pre-training Captures","created":"2019-12-31","cited_by_count":161},{"id":"1906.03741","title":"BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization","created":"2019-06-10","cited_by_count":156},{"id":"2010.07100","title":"Re-evaluating Evaluation in Text Summarization","created":"2020-10-14","cited_by_count":145},{"id":"1711.04434","title":"Faithful to the Original: Fact Aware Neural Abstractive Summarization","created":"2017-11-13","cited_by_count":141},{"id":"2010.08014","title":"GSum: A General Framework for Guided Neural Abstractive Summarization","created":"2020-10-15","cited_by_count":129},{"id":"1904.02792","title":"Unifying Human and Statistical Evaluation for Natural Language Generation","created":"2019-04-04","cited_by_count":127},{"id":"2010.05478","title":"Evaluating Factuality in Generation with Dependency-level Entailment","created":"2020-10-12","cited_by_count":122},{"id":"1910.00523","title":"BillSum: A Corpus for Automatic Summarization of US Legislation","created":"2019-10-01","cited_by_count":117},{"id":"1603.07252","title":"Neural Summarization by Extracting Sentences and Words","created":"2016-03-23","cited_by_count":116},{"id":"1910.14142","title":"Discourse-Aware Neural Extractive Text Summarization","created":"2019-10-30","cited_by_count":105},{"id":"2103.06410","title":"MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization","created":"2021-03-11","cited_by_count":104},{"id":"1905.13322","title":"Assessing The Factual Accuracy of Generated Text","created":"2019-05-30","cited_by_count":103},{"id":"1811.00783","title":"Abstractive Summarization of Reddit Posts with Multi-level Memory Networks","created":"2018-11-02","cited_by_count":102},{"id":"2107.00650","title":"CLIP-It! Language-Guided Video Summarization","created":"2021-07-01","cited_by_count":99},{"id":"2012.04281","title":"CTRLsum: Towards Generic Controllable Text Summarization","created":"2020-12-08","cited_by_count":98},{"id":"1909.01610","title":"Answers Unite! Unsupervised Metrics for Reinforced Summarization Models","created":"2019-09-04","cited_by_count":92},{"id":"1909.03186","title":"On Extractive and Abstractive Neural Document Summarization with Transformer Language Models","created":"2019-09-07","cited_by_count":83},{"id":"2104.06486","title":"MS2: Multi-Document Summarization of Medical Studies","created":"2021-04-13","cited_by_count":80},{"id":"2010.12834","title":"GO FIGURE: A Meta Evaluation of Factuality in Summarization","created":"2020-10-24","cited_by_count":76},{"id":"2110.07356","title":"Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization","created":"2021-09-09","cited_by_count":76},{"id":"1909.01716","title":"ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks","created":"2019-09-04","cited_by_count":73},{"id":"1606.02858","title":"A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task","created":"2016-06-09","cited_by_count":72},{"id":"2005.03724","title":"SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization","created":"2020-05-07","cited_by_count":69},{"id":"1803.01937","title":"ROUGE 2.0: Updated and Improved Measures for Evaluation of Summarization Tasks","created":"2018-03-05","cited_by_count":67},{"id":"1508.06034","title":"Better Summarization Evaluation with Word Embeddings for ROUGE","created":"2015-08-25","cited_by_count":66},{"id":"1906.03508","title":"Sentence Centrality Revisited for Unsupervised Summarization","created":"2019-06-08","cited_by_count":65},{"id":"2010.04529","title":"What Have We Achieved on Text Summarization?","created":"2020-10-09","cited_by_count":65},{"id":"1906.00318","title":"Question Answering as an Automatic Evaluation Metric for News Article Summarization","created":"2019-06-02","cited_by_count":63},{"id":"2102.00176","title":"Can We Automate Scientific Reviewing?","created":"2021-01-30","cited_by_count":63},{"id":"1807.02202","title":"The price of debiasing automatic metrics in natural language evaluation","created":"2018-07-06","cited_by_count":60},{"id":"1909.08089","title":"Extractive Summarization of Long Documents by Combining Global and Local Context","created":"2019-09-17","cited_by_count":59},{"id":"2105.04623","title":"Improving Factual Consistency of Abstractive Summarization via Question Answering","created":"2021-05-10","cited_by_count":55},{"id":"1706.06681","title":"Graph-based Neural Multi-Document Summarization","created":"2017-06-20","cited_by_count":54},{"id":"1803.10357","title":"Deep Communicating Agents for Abstractive Summarization","created":"2018-03-27","cited_by_count":50},{"id":"2105.03664","title":"D2S: Document-to-Slide Generation Via Query-Based Text Summarization","created":"2021-05-08","cited_by_count":50},{"id":"1805.06266","title":"A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss","created":"2018-05-16","cited_by_count":47},{"id":"2105.12544","title":"Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization","created":"2021-05-26","cited_by_count":46},{"id":"1812.02303","title":"Neural Abstractive Text Summarization with Sequence-to-Sequence Models","created":"2018-12-05","cited_by_count":39},{"id":"2010.07882","title":"Understanding Neural Abstractive Summarization Models via Uncertainty","created":"2020-10-15","cited_by_count":34},{"id":"2106.00130","title":"Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents","created":"2021-05-31","cited_by_count":33},{"id":"2104.07545","title":"Hierarchical Learning for Generation with Long Source Sequences","created":"2021-04-15","cited_by_count":32},{"id":"2109.02401","title":"Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization","created":"2021-09-06","cited_by_count":32},{"id":"2109.14394","title":"EDGAR-CORPUS: Billions of Tokens Make The World Go Round","created":"2021-09-29","cited_by_count":32},{"id":"2105.11921","title":"Focus Attention: Promoting Faithfulness and Diversity in Summarization","created":"2021-05-25","cited_by_count":30},{"id":"2109.04609","title":"An Exploratory Study on Long Dialogue Summarization: What Works and What's Next","created":"2021-09-10","cited_by_count":28},{"id":"2105.14064","title":"Controllable Abstractive Dialogue Summarization with Sketch Supervision","created":"2021-05-28","cited_by_count":27},{"id":"2007.08954","title":"SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression","created":"2020-07-17","cited_by_count":24},{"id":"2105.03801","title":"Long-Span Summarization via Local Attention and Content Selection","created":"2021-05-08","cited_by_count":24},{"id":"1809.10735","title":"A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC","created":"2018-09-27","cited_by_count":22},{"id":"1909.04028","title":"Countering the Effects of Lead Bias in News Summarization via Multi-Stage Training and Auxiliary Losses","created":"2019-09-08","cited_by_count":22},{"id":"2010.06253","title":"Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks","created":"2020-10-13","cited_by_count":22},{"id":"2106.13945","title":"A Training-free and Reference-free Summarization Evaluation Metric via Centrality-weighted Relevance and Self-referenced Redundancy","created":"2021-06-26","cited_by_count":21},{"id":"2109.07954","title":"Improving Unsupervised Question Answering via Summarization-Informed Question Generation","created":"2021-09-16","cited_by_count":21},{"id":"2010.01781","title":"Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning","created":"2020-10-05","cited_by_count":17},{"id":"2106.11388","title":"How well do you know your summarization datasets?","created":"2021-06-21","cited_by_count":16},{"id":"2006.10213","title":"SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization","created":"2020-06-18","cited_by_count":15},{"id":"2110.01280","title":"Leveraging Information Bottleneck for Scientific Document Summarization","created":"2021-10-04","cited_by_count":13},{"id":"1908.10383","title":"Facet-Aware Evaluation for Extractive Summarization","created":"2019-08-27","cited_by_count":5},{"id":"2010.09190","title":"SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline","created":"2020-10-19","cited_by_count":5},{"id":"2106.01581","title":"To Point or Not to Point: Understanding How Abstractive Summarizers Paraphrase Text","created":"2021-06-03","cited_by_count":5}],"next_cursor":null}