Corpus ID: 54448559

Neural Abstractive Text Summarization with Sequence-to-Sequence Models

  title={Neural Abstractive Text Summarization with Sequence-to-Sequence Models},
  author={Tian Shi and Yaser Keneshloo and Naren Ramakrishnan and C. Reddy},
  • Tian Shi, Yaser Keneshloo, +1 author C. Reddy
  • Published 2018
  • Computer Science, Mathematics
  • ArXiv
  • In the past few years, neural abstractive text summarization with sequence-to-sequence (seq2seq) models have gained a lot of popularity. [...] Key Method Many models were first proposed for language modeling and generation tasks, such as machine translation, and later applied to abstractive text summarization. Therefore, we also provide a brief review of these models.Expand Abstract
    46 Citations
    Faster Transformers for Document Summarization
    Deep Reinforcement Learning for Sequence-to-Sequence Models
    • 56
    • PDF
    Seq2seq Deep Learning Method for Summary Generation by LSTM with Two-way Encoder and Beam Search Decoder
    • G. Szücs, Dorottya Huszti
    • Computer Science
    • 2019 IEEE 17th International Symposium on Intelligent Systems and Informatics (SISY)
    • 2019
    Efficient Adaptation of Pretrained Transformers for Abstractive Summarization
    • 13
    • Highly Influenced
    • PDF
    LeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization
    • 8
    • PDF
    Study on Abstractive Text Summarization Techniques
    Enhancing a Text Summarization System with ELMo
    • 1
    • PDF


    Diverse Beam Search for Increased Novelty in Abstractive Summarization
    • 6
    • Highly Influential
    • PDF
    Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
    • 1,035
    • Highly Influential
    • PDF
    Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting
    • 225
    • Highly Influential
    • PDF
    Bottom-Up Abstractive Summarization
    • 254
    • PDF
    A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization
    • 55
    • PDF
    Text Summarization with Pretrained Encoders
    • 204
    • PDF
    Towards Improving Abstractive Summarization via Entailment Generation
    • 17
    • PDF
    Deep Reinforcement Learning for Sequence-to-Sequence Models
    • 56
    • PDF