mlsanityData to 14 Oct 2026

CLTS+: A New Chinese Long Text Summarization Dataset with Abstractive Summaries

arXiv 2206.04253 ↗First posted 9 Jun 2022cs.CL cs.AIcited by 4

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Abstract (arXiv)

The abstractive methods lack of creative ability is particularly a problem in automatic text summarization. The summaries generated by models are mostly extracted from the source articles. One of the main causes for this problem is the lack of dataset with abstractiveness, especially for Chinese. In order to solve this problem, we paraphrase the reference summaries in CLTS, the Chinese Long Text Summarization dataset, correct errors of factual inconsistencies, and propose the first Chinese Long Text Summarization dataset with a high level of abstractiveness, CLTS+, which contains more than 180K article-summary pairs and is available online. Additionally, we introduce an intrinsic metric based on co-occurrence words to evaluate the dataset we constructed. We analyze the extraction strategies used in CLTS+ summaries against other datasets to quantify the abstractiveness and difficulty of our new data and train several baselines on CLTS+ to verify the utility of it for improving the creative ability of models.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 18 of its 26 references that are in the map. First posted on arXiv 9 Jun 2022.

Builds on

18 of 26 references.

References · 18

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,292
  4. 2015Teaching Machines to Read and Comprehendcited by 1,049
  5. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 812
  6. 2019SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationcited by 535
  7. 2018A Discourse-Aware Attention Model for Abstractive Summarization of Long Documentscited by 478
  8. 2019Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Modelcited by 431
  9. 2015A Neural Attention Model for Abstractive Sentence Summarizationcited by 396
  10. 2017A Deep Reinforced Model for Abstractive Summarizationcited by 365
  11. 2018Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategiescited by 294
  12. 2017OpenNMT: Open-source Toolkit for Neural Machine Translationcited by 251
6 more
  1. 2016SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documentscited by 246
  2. 2019BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarizationcited by 156
  3. 2019ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networkscited by 73
  4. 2015LCSTS: A Large Scale Chinese Short Text Summarization Datasetcited by 65
  5. 2018Neural Document Summarization by Jointly Learning to Score and Select Sentencescited by 56
  6. 2019A Closer Look at Data Bias in Neural Extractive Summarization Modelscited by 18

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Cited by year

Citing papers per year: 2023: 1; 2024: 3.

4 papers.

  1. 2024A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methodscited by 102
  2. 2023M4LE: A Multi-Ability Multi-Range Multi-Task Multi-Domain Long-Context Evaluation Benchmark for Large Language Modelscited by 15
  3. 2024The State and Fate of Summarization Datasets: A Surveycited by 3
  4. 2024CNNSum: Exploring Long-Context Summarization with Large Language Models in Chinese Novelscited by 2