SOTAVerified

Extractive Text Summarization

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

Papers

Showing 125 of 95 papers

TitleStatusHype
Screenplay Summarization Using Latent Narrative StructureCode1
Efficient Attention: Attention with Linear ComplexitiesCode1
MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision ProcessesCode1
Text Summarization with Pretrained EncodersCode1
DebateSum: A large-scale argument mining and summarization datasetCode1
Extractive Summarization as Text MatchingCode1
Fine-tune BERT for Extractive SummarizationCode1
Heterogeneous Graph Neural Networks for Extractive Document SummarizationCode1
Unsupervised Extractive Summarization by Pre-training Hierarchical TransformersCode1
CX DB8: A queryable extractive summarizer and semantic search engineCode1
Get To The Point: Summarization with Pointer-Generator NetworksCode1
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document SummarizationCode1
Align and Attend: Multimodal Summarization with Dual Contrastive LossesCode1
A topic-based sentence representation for extractive text summarization0
A study of semantic augmentation of word embeddings for extractive summarization0
A New Sentence Extraction Strategy for Unsupervised Extractive Summarization Methods0
A Hybrid PSO-GA for Extractive Text Summarization0
Abstractive Text Summarization for Sanskrit Prose: A Study of Methods and Approaches0
Combining Word Embeddings and N-grams for Unsupervised Document Summarization0
A Hierarchical Structured Self-Attentive Model for Extractive Document Summarization (HSSAS)0
A Novel System for Extractive Clinical Note Summarization using EHR Data0
Considering Nested Tree Structure in Sentence Extractive Summarization with Pre-trained Transformer0
CovSumm: an unsupervised transformer-cum-graph-based hybrid document summarization model for CORD-190
Classify or Select: Neural Architectures for Extractive Document Summarization0
Abstractive and Extractive Text Summarization using Document Context Vector and Recurrent Neural Networks0
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Benchmark Results

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