Automatic News Summerization
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Abstract (arXiv)
Natural Language Processing is booming with its applications in the real world, one of which is Text Summarization for large texts including news articles. This research paper provides an extensive comparative evaluation of extractive and abstractive approaches for news text summarization, with an emphasis on the ROUGE score analysis. The study employs the CNN-Daily Mail dataset, which consists of news articles and human-generated reference summaries. The evaluation employs ROUGE scores to assess the efficacy and quality of generated summaries. After Evaluation, we integrate the best-performing models on a web application to assess their real-world capabilities and user experience.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 17 Oct 2023.
Builds on
9 of 29 references.
References · 9
- 2017Attention Is All You Needcited by 59,932
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
- 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
- 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
- 2015Teaching Machines to Read and Comprehendcited by 1,039
- 2016Neural Summarization by Extracting Sentences and Wordscited by 114
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Citing papers per year: 2025: 1.
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