mlsanityData to 13 Oct 2026

Abstractive Summarization of Reddit Posts with Multi-level Memory Networks

arXiv 1811.00783 ↗First posted 2 Nov 2018cs.CLcited by 102

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Abstract (arXiv)

We address the problem of abstractive summarization in two directions: proposing a novel dataset and a new model. First, we collect Reddit TIFU dataset, consisting of 120K posts from the online discussion forum Reddit. We use such informal crowd-generated posts as text source, in contrast with existing datasets that mostly use formal documents as source such as news articles. Thus, our dataset could less suffer from some biases that key sentences usually locate at the beginning of the text and favorable summary candidates are already inside the text in similar forms. Second, we propose a novel abstractive summarization model named multi-level memory networks (MMN), equipped with multi-level memory to store the information of text from different levels of abstraction. With quantitative evaluation and user studies via Amazon Mechanical Turk, we show the Reddit TIFU dataset is highly abstractive and the MMN outperforms the state-of-the-art summarization models.

Key facts. Cited by 102 papers in the map (data through 13 Oct 2026). First posted on arXiv 2 Nov 2018.

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Citing papers per year: 2021: 16; 2022: 28; 2023: 24; 2024: 22; 2025: 10; 2026: 2.

102 papers.

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  1. 2021Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpuscited by 485
  2. 2022MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledgecited by 473
  3. 2024LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compressioncited by 344
  4. 2022Out-of-Distribution Detection and Selective Generation for Conditional Language Modelscited by 195
  5. 2021CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPcited by 142
  6. 2022CM3: A Causal Masked Multimodal Model of the Internetcited by 142
  7. 2023Unified Demonstration Retriever for In-Context Learningcited by 141
  8. 2022Calibrating Sequence likelihood Improves Conditional Language Generationcited by 121
  9. 2022An Empirical Survey on Long Document Summarization: Datasets, Models and Metricscited by 74
  10. 2021Cross Modal Retrieval with Querybank Normalisationcited by 71
  11. 2022SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarizationcited by 61
  12. 2021HTLM: Hyper-Text Pre-Training and Prompting of Language Modelscited by 58
38 more
  1. 2023Extractive Summarization via ChatGPT for Faithful Summary Generationcited by 58
  2. 2024A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Modelscited by 52
  3. 2021AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarizationcited by 51
  4. 2023Teach LLMs to Personalize -- An Approach inspired by Writing Educationcited by 48
  5. 2024SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Modelscited by 37
  6. 2022A Simple Hash-Based Early Exiting Approach For Language Understanding and Generationcited by 32
  7. 2021Focus Attention: Promoting Faithfulness and Diversity in Summarizationcited by 30
  8. 2023Data Portraits: Recording Foundation Model Training Datacited by 30
  9. 2023On Context Utilization in Summarization with Large Language Modelscited by 30
  10. 2022Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarizationcited by 29
  11. 2024Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Biascited by 28
  12. 2024Position: Measure Dataset Diversity, Don't Just Claim Itcited by 26
  13. 2022How Far are We from Robust Long Abstractive Summarization?cited by 25
  14. 2023On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Studycited by 25
  15. 2021Unsupervised Extractive Summarization using Pointwise Mutual Informationcited by 23
  16. 2021GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generationcited by 20
  17. 2021Enriching and Controlling Global Semantics for Text Summarizationcited by 20
  18. 2024ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publicationscited by 20
  19. 2024Data Contamination Report from the 2024 CONDA Shared Taskcited by 20
  20. 2021RetrievalSum: A Retrieval Enhanced Framework for Abstractive Summarizationcited by 19
  21. 2021Can Transformer Models Measure Coherence In Text? Re-Thinking the Shuffle Testcited by 17
  22. 2021How well do you know your summarization datasets?cited by 16
  23. 2022UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarizationcited by 15
  24. 2021Meta-Transfer Learning for Low-Resource Abstractive Summarizationcited by 14
  25. 2025SimMark: A Robust Sentence-Level Similarity-Based Watermarking Algorithm for Large Language Modelscited by 14
  26. 2022Make The Most of Prior Data: A Solution for Interactive Text Summarization with Preference Feedbackcited by 13
  27. 2022Eliciting and Understanding Cross-Task Skills with Task-Level Mixture-of-Expertscited by 12
  28. 2022MentSum: A Resource for Exploring Summarization of Mental Health Online Postscited by 11
  29. 2023Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?cited by 11
  30. 2024Assessing LLMs for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasingcited by 11
  31. 2021TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Postscited by 10
  32. 2022Controlled Text Reductioncited by 10
  33. 2024Tandem Transformers for Inference Efficient LLMscited by 10
  34. 2024GenAudit: Fixing Factual Errors in Language Model Outputs with Evidencecited by 10
  35. 2022Template-based Abstractive Microblog Opinion Summarisationcited by 9
  36. 2023InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPTcited by 9
  37. 2024GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Modelscited by 9
  38. 2022COLO: A Contrastive Learning based Re-ranking Framework for One-Stage Summarizationcited by 8

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