Automatic News Summerization

Kavach Dheer, Arpit Dhankhar
10/17/2023
cs.CLcs.LG

No confident code implementation found for this paper. 2 possible matches can be shown below.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Evaluating LLMs and Pre-trained Models for Text Summarization Across Diverse Datasets

85% match2025

Unraveling the Capabilities of Language Models in News Summarization

84% match1 repo22025

Event-based evaluation of abstractive news summarization

84% match2025

An Analysis of Abstractive Text Summarization Using Pre-trained Models

84% match2023

Advancements in Natural Language Processing for Automatic Text Summarization

83% match2025

Consistency Evaluation of News Article Summaries Generated by Large (and Small) Language Models

83% match2025

Cite this paper

@article{dheer2023automatic,
  title  = {Automatic News Summerization},
  author = {Kavach Dheer and Arpit Dhankhar},
  year   = {2023},
  eprint = {2310.11520},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.11520v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2310.11520.svg)](https://codeofpaper.com/paper/2310.11520)

Discussion