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Hierarchical Transformers for Multi-Document Summarization

arXiv 1905.13164 ↗First posted 30 May 2019cs.CL cs.AIcited by 132

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Abstract (arXiv)

In this paper, we develop a neural summarization model which can effectively process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner. We represent cross-document relationships via an attention mechanism which allows to share information as opposed to simply concatenating text spans and processing them as a flat sequence. Our model learns latent dependencies among textual units, but can also take advantage of explicit graph representations focusing on similarity or discourse relations. Empirical results on the WikiSum dataset demonstrate that the proposed architecture brings substantial improvements over several strong baselines.

Key facts. Cited by 132 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 May 2019.

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Citing papers per year: 2021: 57; 2022: 29; 2023: 25; 2024: 11; 2025: 10.

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  1. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  2. 2021A Survey of Transformerscited by 399
  3. 2021Recursively Summarizing Books with Human Feedbackcited by 287
  4. 2022Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Textcited by 154
  5. 2023Unlimiformer: Long-Range Transformers with Unlimited Length Inputcited by 127
  6. 2021Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understandingcited by 97
  7. 2021MS2: Multi-Document Summarization of Medical Studiescited by 80
  8. 2021PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarizationcited by 62
  9. 2021Poolingformer: Long Document Modeling with Pooling Attentioncited by 60
  10. 2024A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Modelscited by 52
  11. 2021Attention, please! A survey of Neural Attention Models in Deep Learningcited by 50
  12. 2024CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Modelscited by 50
38 more
  1. 2021H-Transformer-1D: Fast One-Dimensional Hierarchical Attention for Sequencescited by 47
  2. 2021Exploring Neural Models for Query-Focused Summarizationcited by 44
  3. 2022Conditional Generation with a Question-Answering Blueprintcited by 44
  4. 2021MReD: A Meta-Review Dataset for Structure-Controllable Text Generationcited by 42
  5. 2022Neural Approaches to Conversational Information Retrievalcited by 41
  6. 2021Towards mental time travel: a hierarchical memory for reinforcement learning agentscited by 38
  7. 2022Models and Datasets for Cross-Lingual Summarisationcited by 36
  8. 2021TWEETSUMM -- A Dialog Summarization Dataset for Customer Servicecited by 34
  9. 2022Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarizationcited by 29
  10. 2023WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpuscited by 29
  11. 2021Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learningcited by 28
  12. 2022Learning to Revise References for Faithful Summarizationcited by 28
  13. 2023Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answeringcited by 28
  14. 2021Long-Span Summarization via Local Attention and Content Selectioncited by 24
  15. 2021A Deep Metric Learning Approach to Account Linkingcited by 24
  16. 2021EmailSum: Abstractive Email Thread Summarizationcited by 24
  17. 2022Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Searchcited by 23
  18. 2021Commonsense Knowledge Aware Concept Selection For Diverse and Informative Visual Storytellingcited by 22
  19. 2021Factorising Meaning and Form for Intent-Preserving Paraphrasingcited by 22
  20. 2022HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarizationcited by 22
  21. 2021Convex Aggregation for Opinion Summarizationcited by 21
  22. 2021BASS: Boosting Abstractive Summarization with Unified Semantic Graphcited by 21
  23. 2021Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarizationcited by 21
  24. 2022Target-aware Abstractive Related Work Generation with Contrastive Learningcited by 20
  25. 2021MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processescited by 19
  26. 2021Multiplex Graph Neural Network for Extractive Text Summarizationcited by 19
  27. 2021Topic-Guided Abstractive Multi-Document Summarizationcited by 19
  28. 2021Data Augmentation for Abstractive Query-Focused Multi-Document Summarizationcited by 18
  29. 2021Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notescited by 18
  30. 2021Self-Supervised Multimodal Opinion Summarizationcited by 18
  31. 2021Local Citation Recommendation with Hierarchical-Attention Text Encoder and SciBERT-based Rerankingcited by 18
  32. 2023Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluationscited by 18
  33. 2021CDLM: Cross-Document Language Modelingcited by 17
  34. 2021PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification Modelscited by 17
  35. 2024Abstractive Text Summarization: State of the Art, Challenges, and Improvementscited by 17
  36. 2021Probing Product Description Generation via Posterior Distillationcited by 15
  37. 2022Klexikon: A German Dataset for Joint Summarization and Simplificationcited by 15
  38. 2021User-Inspired Posterior Network for Recommendation Reason Generationcited by 14

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