SOTAVerified

Extractive Text Summarization

Given a document, selecting a subset of the words or sentences which best represents a summary of the document.

Papers

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Showing 1–10 of 95 papers

TitleStatusHype
A Novel Word Pair-based Gaussian Sentence Similarity Algorithm For Bengali Extractive Text SummarizationCode0
Scaling Up Summarization: Leveraging Large Language Models for Long Text Extractive Summarization—0
A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches—0
RankSum An unsupervised extractive text summarization based on rank fusion—0
Prompt-based Pseudo-labeling Strategy for Sample-Efficient Semi-Supervised Extractive Summarization—0
Pre-training Meets Clustering: A Hybrid Extractive Multi-document Summarization ModelCode0
CovSumm: an unsupervised transformer-cum-graph-based hybrid document summarization model for CORD-19—0
San-BERT: Extractive Summarization for Sanskrit Documents using BERT and it's variants—0
Align and Attend: Multimodal Summarization with Dual Contrastive LossesCode1
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation—0
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All datasetsCNN / Daily MailDebateSumGovReportDUC 2004DUC 2004 Task 1

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Longformer-BaseROUGE-L57.21—Unverified
2GPT2-MediumROUGE-L53.23—Unverified
3BERT-LargeROUGE-L49.98—Unverified