mlsanityData to 7 Oct 2026

Summarization as Indirect Supervision for Relation Extraction

arXiv 2205.09837 ↗First posted 19 May 2022cs.CL cs.LGcited by 31

Not on the map. Why?

Abstract (arXiv)

Relation extraction (RE) models have been challenged by their reliance on training data with expensive annotations. Considering that summarization tasks aim at acquiring concise expressions of synoptical information from the longer context, these tasks naturally align with the objective of RE, i.e., extracting a kind of synoptical information that describes the relation of entity mentions. We present SuRE, which converts RE into a summarization formulation. SuRE leads to more precise and resource-efficient RE based on indirect supervision from summarization tasks. To achieve this goal, we develop sentence and relation conversion techniques that essentially bridge the formulation of summarization and RE tasks. We also incorporate constraint decoding techniques with Trie scoring to further enhance summarization-based RE with robust inference. Experiments on three RE datasets demonstrate the effectiveness of SuRE in both full-dataset and low-resource settings, showing that summarization is a promising source of indirect supervision to improve RE models.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 43 of its 59 references that are in the map. First posted on arXiv 19 May 2022.

Builds on

43 of 59 references.

References · 43

  1. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  2. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  3. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,308
  4. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  5. 2015Teaching Machines to Read and Comprehendcited by 1,039
  6. 2019SpanBERT: Improving Pre-training by Representing and Predicting Spanscited by 832
  7. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
  8. 2019ERNIE: Enhanced Language Representation with Informative Entitiescited by 525
  9. 2018A Discourse-Aware Attention Model for Abstractive Summarization of Long Documentscited by 477
  10. 2017Zero-Shot Relation Extraction via Reading Comprehensioncited by 425
  11. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 386
  12. 2020Autoregressive Entity Retrievalcited by 333
31 more
  1. 2020LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentioncited by 326
  2. 2020K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapterscited by 318
  3. 2019Knowledge Enhanced Contextual Word Representationscited by 294
  4. 2019Matching the Blanks: Distributional Similarity for Relation Learningcited by 271
  5. 2021PTR: Prompt Tuning with Rules for Text Classificationcited by 238
  6. 2021Structured Prediction as Translation between Augmented Natural Languagescited by 183
  7. 2021KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extractioncited by 180
  8. 2020Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Poolingcited by 115
  9. 2020Knowledge-Grounded Dialogue Generation with Pre-trained Language Modelscited by 107
  10. 2020TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Taskcited by 96
  11. 2019Coreference Resolution as Query-based Span Predictioncited by 91
  12. 2018Ultra-Fine Entity Typingcited by 87
  13. 2019Enriching Pre-trained Language Model with Entity Information for Relation Classificationcited by 82
  14. 2021An Improved Baseline for Sentence-level Relation Extractioncited by 80
  15. 2020Learning from Context or Names? An Empirical Study on Neural Relation Extractioncited by 75
  16. 2021Planning with Learned Entity Prompts for Abstractive Summarizationcited by 75
  17. 2021DocNLI: A Large-scale Dataset for Document-level Natural Language Inferencecited by 75
  18. 2021Re-TACRED: Addressing Shortcomings of the TACRED Datasetcited by 73
  19. 2021Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extractioncited by 73
  20. 2019SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominalscited by 63
  21. 2019CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learningcited by 47
  22. 2020Contrastive Triple Extraction with Generative Transformercited by 44
  23. 2020Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Startcited by 44
  24. 2022Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extractioncited by 40
  25. 2022Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuningcited by 34
  26. 2021Document-level Entity-based Extraction as Template Generationcited by 33
  27. 2021Relation Classification with Entity Type Restrictioncited by 31
  28. 2018Incorporating Structured Commonsense Knowledge in Story Completioncited by 23
  29. 2022Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inferencecited by 17
  30. 2021Discourse-level Relation Extraction via Graph Poolingcited by 9
  31. 2020Foreseeing the Benefits of Incidental Supervisioncited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 16; 2024: 7; 2025: 1; 2026: 1.

31 papers.

Sort:

By area:

  1. 2023Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!cited by 119
  2. 2023LLMaAA: Making Large Language Models as Active Annotatorscited by 78
  3. 2023Context-faithful Prompting for Large Language Modelscited by 76
  4. 2023Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractorscited by 44
  5. 2023Revisiting Large Language Models as Zero-shot Relation Extractorscited by 29
  6. 2023LIQUID: A Framework for List Question Answering Dataset Generationcited by 19
  7. 2022TAGPRIME: A Unified Framework for Relational Structure Extractioncited by 15
  8. 2022Salience Allocation as Guidance for Abstractive Summarizationcited by 14
  9. 2022Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?cited by 13
  10. 2023GDA: Generative Data Augmentation Techniques for Relation Extraction Taskscited by 10
  11. 2024Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Promptingcited by 10
  12. 2023Contextual Label Projection for Cross-Lingual Structured Predictioncited by 9
19 more
  1. 2023Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairscited by 9
  2. 2023Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuningcited by 8
  3. 2025A Survey on Retrieval And Structuring Augmented Generation with Large Language Modelscited by 8
  4. 2024Improving Event Definition Following For Zero-Shot Event Detectioncited by 7
  5. 2023Think Rationally about What You See: Continuous Rationale Extraction for Relation Extractioncited by 6
  6. 2023STAR: Boosting Low-Resource Information Extraction by Structure-to-Text Data Generation with Large Language Modelscited by 6
  7. 2022Improving Distantly Supervised Relation Extraction by Natural Language Inferencecited by 3
  8. 2023Dense Retrieval as Indirect Supervision for Large-space Decision Makingcited by 3
  9. 2023Enhancing Low-Resource Relation Representations through Multi-View Decouplingcited by 3
  10. 2024On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extractioncited by 3
  11. 2022Continual Contrastive Finetuning Improves Low-Resource Relation Extractioncited by 2
  12. 2023CEO: Corpus-based Open-Domain Event Ontology Inductioncited by 2
  13. 2024Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluationcited by 2
  14. 2024Pointing out the Shortcomings of Relation Extraction Models with Semantically Motivated Adversarialscited by 1
  15. 2024X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classificationcited by 1
  16. 2022Learning with Silver Standard Data for Zero-shot Relation Extractioncited by 0
  17. 2023Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extractioncited by 0
  18. 2024End-to-End Trainable Retrieval-Augmented Generation for Relation Extractioncited by 0
  19. 2026PiPMRE: A Pipeline Based on Language Model for Medical Relation Extractioncited by 0