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LCSTS: A Large Scale Chinese Short Text Summarization Dataset

arXiv 1506.05865 ↗First posted 19 Jun 2015cs.CL cs.IR cs.LGcited by 65

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Abstract (arXiv)

Automatic text summarization is widely regarded as the highly difficult problem, partially because of the lack of large text summarization data set. Due to the great challenge of constructing the large scale summaries for full text, in this paper, we introduce a large corpus of Chinese short text summarization dataset constructed from the Chinese microblogging website Sina Weibo, which is released to the public {http://icrc.hitsz.edu.cn/Article/show/139.html}. This corpus consists of over 2 million real Chinese short texts with short summaries given by the author of each text. We also manually tagged the relevance of 10,666 short summaries with their corresponding short texts. Based on the corpus, we introduce recurrent neural network for the summary generation and achieve promising results, which not only shows the usefulness of the proposed corpus for short text summarization research, but also provides a baseline for further research on this topic.

Key facts. Cited by 65 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Jun 2015.

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Citing papers per year: 2021: 13; 2022: 17; 2023: 10; 2024: 11; 2025: 11; 2026: 3.

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  1. 2023A Comprehensive Overview of Large Language Modelscited by 648
  2. 2021ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generationcited by 260
  3. 2021The GEM Benchmark: Natural Language Generation, its Evaluation and Metricscited by 239
  4. 2024A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methodscited by 102
  5. 2021CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generationcited by 81
  6. 2024Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learningcited by 53
  7. 2021CPM-2: Large-scale Cost-effective Pre-trained Language Modelscited by 52
  8. 2022CSL: A Large-scale Chinese Scientific Literature Datasetcited by 45
  9. 2022A Survey on Cross-Lingual Summarizationcited by 38
  10. 2025KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Modelcited by 36
  11. 2023HC3 Plus: A Semantic-Invariant Human ChatGPT Comparison Corpuscited by 35
  12. 2021ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generationcited by 31
38 more
  1. 2025URO-Bench: Towards Comprehensive Evaluation for End-to-End Spoken Dialogue Modelscited by 30
  2. 2022AraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive Summarizationcited by 24
  3. 2024Improving Attributed Text Generation of Large Language Models via Preference Learningcited by 23
  4. 2022WeLM: A Well-Read Pre-trained Language Model for Chinesecited by 19
  5. 2022A Variational Hierarchical Model for Neural Cross-Lingual Summarizationcited by 17
  6. 2022Automatic Text Summarization Methods: A Comprehensive Reviewcited by 17
  7. 2023PanGu-π: Enhancing Language Model Architectures via Nonlinearity Compensationcited by 17
  8. 2024Abstractive Text Summarization: State of the Art, Challenges, and Improvementscited by 17
  9. 2021CUGE: A Chinese Language Understanding and Generation Evaluation Benchmarkcited by 16
  10. 2023M4LE: A Multi-Ability Multi-Range Multi-Task Multi-Domain Long-Context Evaluation Benchmark for Large Language Modelscited by 15
  11. 2024HRLAIF: Improvements in Helpfulness and Harmlessness in Open-domain Reinforcement Learning From AI Feedbackcited by 15
  12. 2021Evaluating the Efficacy of Summarization Evaluation across Languagescited by 14
  13. 2021Text Compression-aided Transformer Encodingcited by 13
  14. 2022CSCD-NS: a Chinese Spelling Check Dataset for Native Speakerscited by 13
  15. 2026F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual Worldcited by 12
  16. 2025SAEMark: Steering Personalized Multilingual LLM Watermarks with Sparse Autoencoderscited by 7
  17. 2021Summarising Historical Text in Modern Languagescited by 6
  18. 2021BEAMetrics: A Benchmark for Language Generation Evaluation Evaluationcited by 6
  19. 2021Unifying Cross-lingual Summarization and Machine Translation with Compression Ratecited by 5
  20. 2021CNewSum: A Large-scale Chinese News Summarization Dataset with Human-annotated Adequacy and Deducibility Levelcited by 5
  21. 2021DSGPT: Domain-Specific Generative Pre-Training of Transformers for Text Generation in E-commerce Title and Review Summarizationcited by 5
  22. 2024PersonalSum: A User-Subjective Guided Personalized Summarization Dataset for Large Language Modelscited by 5
  23. 2022CLTS+: A New Chinese Long Text Summarization Dataset with Abstractive Summariescited by 4
  24. 2023A New Dataset and Empirical Study for Sentence Simplification in Chinesecited by 4
  25. 2024A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generationcited by 4
  26. 2025AQuilt: Weaving Logic and Self-Inspection into Low-Cost, High-Relevance Data Synthesis for Specialist LLMscited by 4
  27. 2022ClueGraphSum: Let Key Clues Guide the Cross-Lingual Abstractive Summarizationcited by 3
  28. 2022SkillNet-NLG: General-Purpose Natural Language Generation with a Sparsely Activated Approachcited by 3
  29. 2022MaskEval: Weighted MLM-Based Evaluation for Text Summarization and Simplificationcited by 3
  30. 2022TextBox 2.0: A Text Generation Library with Pre-trained Language Modelscited by 3
  31. 2023HonestBait: Forward References for Attractive but Faithful Headline Generationcited by 3
  32. 2024The State and Fate of Summarization Datasets: A Surveycited by 3
  33. 2025On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Surveycited by 3
  34. 2026RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agentscited by 3
  35. 2022Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmarkcited by 2
  36. 2022To Adapt or to Fine-tune: A Case Study on Abstractive Summarizationcited by 2
  37. 2023PROM: A Phrase-level Copying Mechanism with Pre-training for Abstractive Summarizationcited by 2
  38. 2024Learning from Mistakes: Self-correct Adversarial Training for Chinese Unnatural Text Correctioncited by 2

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