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 51–75 of 95 papers

TitleStatusHype
Towards Supervised Extractive Text Summarization via RNN-based Sequence Classification—0
Twitter Topic Summarization by Ranking Tweets using Social Influence and Content Quality—0
Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization—0
Using Document Summarization Techniques for Speech Data Subset Selection—0
Using Statistical and Semantic Models for Multi-Document Summarization—0
Using Supervised Bigram-based ILP for Extractive Summarization—0
Extractive Text Summarization Using Generalized Additive Models with Interactions for Sentence Selection—0
Extractive Text Summarization using Neural Networks—0
Extractive Text Summarization with Latent Topics using Heterogeneous Graph Neural Network—0
Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization—0
GUSUM: Graph-Based Unsupervised Summarization using Sentence-BERT and Sentence Features—0
HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization—0
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information—0
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information—0
Hybrid MemNet for Extractive Summarization—0
Improving Neural Text Summarization using Knowledge Graphs—0
Jointly Learning Salience and Redundancy by Adaptive Sentence Reranking for Extractive Summarization—0
Knowledge Graph and Deep Neural Network for Extractive Text Summarization by Utilizing Triples—0
Language Model Pre-training for Hierarchical Document Representations—0
Learning with fuzzy hypergraphs: a topical approach to query-oriented text summarization—0
A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches—0
Monolingual versus Multilingual BERTology for Vietnamese Extractive Multi-Document Summarization—0
MultiGBS: A multi-layer graph approach to biomedical summarization—0
Multi-layered graph-based multi-document summarization model—0
Extractive Multi-document Summarization using K-means, Centroid-based Method, MMR, and Sentence PositionCode0
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All datasetsCNN / Daily MailDebateSumGovReportDUC 2004DUC 2004 Task 1

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HAHSumROUGE-144.68—Unverified
2Scaled-MatchSumROUGE-144.51—Unverified
3MatchSumROUGE-144.41—Unverified
4A2SummROUGE-144.11—Unverified
5NeRoBERTaROUGE-143.86—Unverified
6BERT-ext + RLROUGE-142.76—Unverified
7PNBERTROUGE-142.69—Unverified
8HIBERTROUGE-142.37—Unverified
9HERROUGE-142.3—Unverified
10NeuSUMROUGE-141.59—Unverified
#ModelMetricClaimedVerifiedStatus
1Longformer-BaseROUGE-L57.21—Unverified
2GPT2-MediumROUGE-L53.23—Unverified
3BERT-LargeROUGE-L49.98—Unverified
#ModelMetricClaimedVerifiedStatus
1MemSum (extractive)Avg. Test Rouge159.43—Unverified
2HEPOSAvg. Test Rouge156.86—Unverified
#ModelMetricClaimedVerifiedStatus
1Pre-training-meets-Clustering-A-Hybrid-Extractive-Multi-Document-Summarization-ModelTest ROGUE-134.01—Unverified
#ModelMetricClaimedVerifiedStatus
1AbsROUGE-126.55—Unverified