SOTAVerified

Extractive Text Summarization

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 26–50 of 95 papers

TitleStatusHype
IDN-Sum: A New Dataset for Interactive Digital Narrative Extractive Text SummarisationCode0
Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization—0
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information—0
Extractive Text Summarization with Latent Topics using Heterogeneous Graph Neural Network—0
GUSUM: Graph-Based Unsupervised Summarization using Sentence-BERT and Sentence Features—0
A New Sentence Extraction Strategy for Unsupervised Extractive Summarization Methods—0
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information—0
Considering Nested Tree Structure in Sentence Extractive Summarization with Pre-trained Transformer—0
A Hybrid PSO-GA for Extractive Text Summarization—0
Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization—0
Monolingual versus Multilingual BERTology for Vietnamese Extractive Multi-Document Summarization—0
Multiplex Graph Neural Network for Extractive Text Summarization—0
Jointly Learning Salience and Redundancy by Adaptive Sentence Reranking for Extractive Summarization—0
Topic Modeling Based Extractive Text Summarization—0
Knowledge Graph and Deep Neural Network for Extractive Text Summarization by Utilizing Triples—0
Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network—0
Improving Neural Text Summarization using Knowledge Graphs—0
Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks—0
MultiGBS: A multi-layer graph approach to biomedical summarization—0
Abstractive Text Summarization for Sanskrit Prose: A Study of Methods and Approaches—0
Towards a Reliable and Robust Methodology for Crowd-Based Subjective Quality Assessment of Query-Based Extractive Text Summarization—0
Combining Word Embeddings and N-grams for Unsupervised Document Summarization—0
Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model—0
At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization—0
Hybrid MemNet for Extractive Summarization—0
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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