AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization
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[1] Dan Su,et al. Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance , 2021, FINDINGS.
[2] Jianfeng Gao,et al. Data Augmentation for Abstractive Query-Focused Multi-Document Summarization , 2021, AAAI.
[3] Mirella Lapata,et al. Generating Query Focused Summaries from Query-Free Resources , 2020, ACL.
[4] Mirella Lapata,et al. Coarse-to-Fine Query Focused Multi-Document Summarization , 2020, EMNLP.
[5] Wei Zhang,et al. Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention , 2020, FINDINGS.
[6] Shafiq R. Joty,et al. Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation , 2020, NAACL.
[7] Mohit Bansal,et al. SuperPAL: Supervised Proposition ALignment for Multi-Document Summarization and Derivative Sub-Tasks , 2020, ArXiv.
[8] Yaliang Li,et al. Bridging Hierarchical and Sequential Context Modeling for Question-driven Extractive Answer Summarization , 2020, SIGIR.
[9] John Glover,et al. A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal , 2020, ACL.
[10] Tanmoy Chakraborty,et al. Neural Abstractive Summarization with Structural Attention , 2020, IJCAI.
[11] Norbert Zeh,et al. Enhancement of Short Text Clustering by Iterative Classification , 2020, NLDB.
[12] Jiawei Han,et al. Generating Representative Headlines for News Stories , 2020, WWW.
[13] Wai Lam,et al. Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering , 2019, AAAI.
[14] Omer Levy,et al. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension , 2019, ACL.
[15] Colin Raffel,et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer , 2019, J. Mach. Learn. Res..
[16] Eduard Hovy,et al. Earlier Isn’t Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization , 2019, EMNLP.
[17] Thomas Demeester,et al. A Self-Training Approach for Short Text Clustering , 2019, RepL4NLP@ACL.
[18] Omer Levy,et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.
[19] Min Yang,et al. A Multi-Task Learning Framework for Abstractive Text Summarization , 2019, AAAI.
[20] Jason Weston,et al. ELI5: Long Form Question Answering , 2019, ACL.
[21] Dragomir R. Radev,et al. Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model , 2019, ACL.
[22] Ido Dagan,et al. Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference , 2019, ACL.
[23] W. Bruce Croft,et al. ANTIQUE: A Non-factoid Question Answering Benchmark , 2019, ECIR.
[24] Tanmoy Chakraborty,et al. CQASUMM: Building References for Community Question Answering Summarization Corpora , 2018, COMAD/CODS.
[25] Mirella Lapata,et al. Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization , 2018, EMNLP.
[26] Ramakanth Pasunuru,et al. Multi-Reward Reinforced Summarization with Saliency and Entailment , 2018, NAACL.
[27] Lukasz Kaiser,et al. Generating Wikipedia by Summarizing Long Sequences , 2018, ICLR.
[28] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[29] Richard Socher,et al. A Deep Reinforced Model for Abstractive Summarization , 2017, ICLR.
[30] M. de Rijke,et al. Summarizing Answers in Non-Factoid Community Question-Answering , 2017, WSDM.
[31] Peng Wang,et al. Self-Taught Convolutional Neural Networks for Short Text Clustering , 2017, Neural Networks.
[32] Vaibhava Goel,et al. Self-Critical Sequence Training for Image Captioning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Bowen Zhou,et al. Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond , 2016, CoNLL.
[34] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Marc'Aurelio Ranzato,et al. Sequence Level Training with Recurrent Neural Networks , 2015, ICLR.
[36] Noah A. Smith,et al. Extractive Summarization by Maximizing Semantic Volume , 2015, EMNLP.
[37] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[38] Claire Cardie,et al. Query-Focused Opinion Summarization for User-Generated Content , 2014, COLING.
[39] Tat-Seng Chua,et al. Community Answer Summarization for Multi-Sentence Question with Group L1 Regularization , 2012, ACL.
[40] Yong Yu,et al. Understanding and Summarizing Answers in Community-Based Question Answering Services , 2008, COLING.
[41] Chin-Yew Lin,et al. ROUGE: A Package for Automatic Evaluation of Summaries , 2004, ACL 2004.
[42] Ronald J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[43] Ronald J. Williams,et al. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.
[44] Vasudeva Varma,et al. Summarizing Answers for Community Question Answer Services , 2013, GSCL.
[45] G. Carenini,et al. A Publicly Available Annotated Corpus for Supervised Email Summarization , 2008 .