mlsanityData to 7 Oct 2026

ComSum: Commit Messages Summarization and Meaning Preservation

arXiv 2108.10763 ↗First posted 23 Aug 2021cs.CL cs.LG cs.SEcited by 3

Not on the map. Why?

Abstract (arXiv)

We present ComSum, a data set of 7 million commit messages for text summarization. When documenting commits, software code changes, both a message and its summary are posted. We gather and filter those to curate developers' work summarization data set. Along with its growing size, practicality and challenging language domain, the data set benefits from the living field of empirical software engineering. As commits follow a typology, we propose to not only evaluate outputs by Rouge, but by their meaning preservation.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 40 of its 98 references that are in the map. First posted on arXiv 23 Aug 2021.

Builds on

40 of 98 references.

References · 40

  1. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  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. 2019BERT Rediscovers the Classical NLP Pipelinecited by 1,046
  6. 2015Teaching Machines to Read and Comprehendcited by 1,039
  7. 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 979
  8. 2017A Closer Look at Memorization in Deep Networkscited by 936
  9. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
  10. 2019Evaluating the Factual Consistency of Abstractive Text Summarizationcited by 578
  11. 2019BLiMP: The Benchmark of Linguistic Minimal Pairs for Englishcited by 553
  12. 2018A Discourse-Aware Attention Model for Abstractive Summarization of Long Documentscited by 477
28 more
  1. 2019Does Learning Require Memorization? A Short Tale about a Long Tailcited by 452
  2. 2015A Neural Attention Model for Abstractive Sentence Summarizationcited by 394
  3. 2018Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategiescited by 292
  4. 2021Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metricscited by 285
  5. 2016Data Programming: Creating Large Training Sets, Quicklycited by 246
  6. 2020ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-trainingcited by 221
  7. 2021BookSum: A Collection of Datasets for Long-form Narrative Summarizationcited by 184
  8. 2019BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarizationcited by 155
  9. 2020Re-evaluating Evaluation in Text Summarizationcited by 144
  10. 2021Q2: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answeringcited by 131
  11. 2020GSum: A General Framework for Guided Neural Abstractive Summarizationcited by 129
  12. 2020ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformationscited by 101
  13. 2020GO FIGURE: A Meta Evaluation of Factuality in Summarizationcited by 75
  14. 2020Analyzing the Source and Target Contributions to Predictions in Neural Machine Translationcited by 71
  15. 2017A parallel corpus of Python functions and documentation strings for automated code documentation and code generationcited by 56
  16. 2018Retrieval-Based Neural Code Generationcited by 41
  17. 2020Learning to Update Natural Language Comments Based on Code Changescited by 30
  18. 2018Automatic Metric Validation for Grammatical Error Correctioncited by 17
  19. 2018Inherent Biases in Reference based Evaluation for Grammatical Error Correction and Text Simplificationcited by 15
  20. 2019Automatically Extracting Challenge Sets for Non local Phenomena in Neural Machine Translationcited by 15
  21. 2021Mediators in Determining what Processing BERT Performs Firstcited by 14
  22. 2020Abstractive Snippet Generationcited by 11
  23. 2021Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attributioncited by 10
  24. 2018Reference-less Measure of Faithfulness for Grammatical Error Correctioncited by 9
  25. 2019Fine-grained evaluation of German-English Machine Translation based on a Test Suitecited by 6
  26. 2018Machine Learning in Cyber-Security - Problems, Challenges and Data Setscited by 5
  27. 2021To Point or Not to Point: Understanding How Abstractive Summarizers Paraphrase Textcited by 5
  28. 2021Follow Your Nose -- Which Code Smells are Worth Chasing?cited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 1; 2026: 1.

3 papers.

  1. 2021End to End Software Engineering Researchcited by 2
  2. 2025LibRec: Benchmarking Retrieval-Augmented LLMs for Library Migration Recommendationscited by 0
  3. 2026Which Alert Removals are Beneficial?cited by 0