News Summarization
31 papers with code • 1 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in News Summarization
| Trend | Dataset | Best Model | Paper | Code | Compare |
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ECTSum
DICE: a Dataset of Italian Crime Event news
FIB
Most implemented papers
IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation
gsarti/it5
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The T5 model and its unified text-to-text paradigm contributed in advancing the state-of-the-art for many natural language processing tasks.
ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
Personalized content-based recommender systems have become indispensable tools for users to navigate through the vast amount of content available on platforms like daily news websites and book recommendation services.
Sentence Centrality Revisited for Unsupervised Summarization
Single document summarization has enjoyed renewed interests in recent years thanks to the popularity of neural network models and the availability of large-scale datasets.
Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization
dykang/biassum
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IJCNLP 2019
We find that while position exhibits substantial bias in news articles, this is not the case, for example, with academic papers and meeting minutes.
Exploring Content Selection in Summarization of Novel Chapters
We present a new summarization task, generating summaries of novel chapters using summary/chapter pairs from online study guides.
A Cascade Approach to Neural Abstractive Summarization with Content Selection and Fusion
We present an empirical study in favor of a cascade architecture to neural text summarization.
Bengali Abstractive News Summarization(BANS): A Neural Attention Approach
Prithwiraj12/Bengali-Deep-News-Summarization
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Our proposed system deploys a local attention-based model that produces a long sequence of words with lucid and human-like generated sentences with noteworthy information of the original document.
Generating abstractive summaries of Lithuanian news articles using a transformer model
In this work, we train the first monolingual Lithuanian transformer model on a relatively large corpus of Lithuanian news articles and compare various output decoding algorithms for abstractive news summarization.
The Summary Loop: Learning to Write Abstractive Summaries Without Examples
cannylab/summary_loop
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ACL 2020
This work presents a new approach to unsupervised abstractive summarization based on maximizing a combination of coverage and fluency for a given length constraint.
MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization
xinnuoxu/miranews
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Findings (EMNLP) 2021
We show via data analysis that it's not only the models which are to blame: more than 27% of facts mentioned in the gold summaries of MiRANews are better grounded on assisting documents than in the main source articles.
