SOTAVerified

Abstractive Text Summarization

Abstractive Text Summarization is the task of generating a short and concise summary that captures the salient ideas of the source text. The generated summaries potentially contain new phrases and sentences that may not appear in the source text.

Source: Generative Adversarial Network for Abstractive Text Summarization

Image credit: Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond

Papers

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Showing 251–300 of 846 papers

TitleStatusHype
ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument MiningCode1
Entity-based De-noising Modeling for Controllable Dialogue Summarization—0
Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization—0
To Adapt or to Fine-tune: A Case Study on Abstractive SummarizationCode0
Z-Code++: A Pre-trained Language Model Optimized for Abstractive SummarizationCode4
Abstractive Meeting Summarization: A SurveyCode1
Forming Trees with Treeformers—0
Meta-Learning the Difference: Preparing Large Language Models for Efficient AdaptationCode0
Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control—0
Few-shot fine-tuning SOTA summarization models for medical dialogues—0
DACSA: A large-scale Dataset for Automatic summarization of Catalan and Spanish newspaper Articles—0
Jointly Learning Guidance Induction and Faithful Summary Generation via Conditional Variational Autoencoders—0
Semantic-Preserving Abstractive Text Summarization with Siamese Generative Adversarial Net—0
Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple GranularitiesCode1
0/1 Deep Neural Networks via Block Coordinate Descent—0
Indian Legal Text Summarization: A Text Normalisation-based Approach—0
Multilingual Financial Documentation Summarization by Team_Tredence for FNS2022—0
TeSum: Human-Generated Abstractive Summarization Corpus for TeluguCode0
CATAMARAN: A Cross-lingual Long Text Abstractive Summarization Dataset—0
Multilingual Text Summarization on Financial Documents—0
The Bull and the Bear: Summarizing Stock Market Discussions—0
Extractive and Abstractive Summarization Methods for Financial Narrative Summarization in English, Spanish and Greek—0
Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text SummarizationCode0
Leveraging Locality in Abstractive Text SummarizationCode1
Counterfactual Data Augmentation improves Factuality of Abstractive Summarization—0
Factorizing Content and Budget Decisions in Abstractive Summarization of Long DocumentsCode1
Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error DetectorsCode1
Lossless Acceleration for Seq2seq Generation with Aggressive DecodingCode0
FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive SummarizationCode1
ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language GenerationCode1
Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in SummarizationCode0
Few-shot Mining of Naturally Occurring Inputs and Outputs—0
Masked Summarization to Generate Factually Inconsistent Summaries for Improved Factual Consistency CheckingCode1
Efficient Few-Shot Fine-Tuning for Opinion SummarizationCode0
RNSum: A Large-Scale Dataset for Automatic Release Note Generation via Commit Logs Summarization—0
Length Control in Abstractive Summarization by Pretraining Information SelectionCode0
FilipN@LT-EDI-ACL2022-Detecting signs of Depression from Social Media: Examining the use of summarization methods as data augmentation for text classificationCode0
NLPOP: a Dataset for Popularity Prediction of Promoted NLP Research on TwitterCode0
Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine TranslationCode1
Meta-X_NLG: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation—0
Large-Scale Multi-Document Summarization with Information Extraction and Compression—0
Two New Datasets for Italian-Language Abstractive Text SummarizationCode0
Faithful to the Document or to the World? Mitigating Hallucinations via Entity-linked Knowledge in Abstractive Summarization—0
WikiMulti: a Corpus for Cross-Lingual SummarizationCode0
Spurious Correlations in Reference-Free Evaluation of Text Generation—0
A Survey on Neural Abstractive Summarization Methods and Factual Consistency of Summarization—0
Factual Error Correction for Abstractive Summaries Using Entity Retrieval—0
FactGraph: Evaluating Factuality in Summarization with Semantic Graph RepresentationsCode1
PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization—0
Abstractive summarization of hospitalisation histories with transformer networks—0
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All datasetsCNN / Daily MailAbstractive Text Summarization from Il PostvietnewsAbstractive Text Summarization from FanpageEDUsumCNN/Daily MailMLSum-itWITSAESLCeLifeInshorts NewsMLSUM de

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Scrambled code + broken (alter)ROUGE-148.18—Unverified
2BRIOROUGE-147.78—Unverified
3PegasusROUGE-147.36—Unverified
4PEGASUS + SummaRerankerROUGE-147.16—Unverified
5Scrambled code + brokenROUGE-146.71—Unverified
6BART + SimCLSROUGE-146.67—Unverified
7SEASONROUGE-146.27—Unverified
8Fourier TransformerROUGE-144.76—Unverified
9GLM-XXLargeROUGE-144.7—Unverified
10BART + R-DropROUGE-144.51—Unverified
#ModelMetricClaimedVerifiedStatus
1mBARTROUGE-138.91—Unverified
2mBARTROUGE-138.91—Unverified
3BART-ITROUGE-137.31—Unverified
4mT5ROUGE-135.04—Unverified
5IT5ROUGE-133.78—Unverified
6IT5-baseROUGE-132.88—Unverified
7Pegasus-CNN/DM (eng-it translation)ROUGE-123.96—Unverified
8Pegasus-XSum (eng-it translation)ROUGE-121.03—Unverified
#ModelMetricClaimedVerifiedStatus
1Kết quả nghiên cứuRouge-167.8—Unverified
2ViT5 largeRouge-163.37—Unverified
3ViT5 baseRouge-162.77—Unverified
4BARTphoRouge-161.14—Unverified
5mBARTRouge-159.81—Unverified
6mT5Rouge-158.05—Unverified
7TransformerRouge-157.56—Unverified
#ModelMetricClaimedVerifiedStatus
1mBARTROUGE-136.52—Unverified
2mBARTROUGE-136.5—Unverified
3BART-ITROUGE-135.42—Unverified
4mT5ROUGE-134.13—Unverified
5IT5-baseROUGE-133.99—Unverified
6IT5ROUGE-133.83—Unverified
#ModelMetricClaimedVerifiedStatus
1GP_Step_SimROUGE-164.48—Unverified
2NEZHAROUGE-163.91—Unverified
3RoBERTaROUGE-163.22—Unverified
4BERTROUGE-162.37—Unverified
5Seq2seqROUGE-148.62—Unverified
#ModelMetricClaimedVerifiedStatus
1BART (TextBox 2.0)ROUGE-144.47—Unverified
2SEGMENTROUGE-142.1—Unverified
3CriSPO 3-shotROUGE-142.1—Unverified
4Claude Instant + SigExtROUGE-142—Unverified
#ModelMetricClaimedVerifiedStatus
1mBARTrouge119.35—Unverified
2IT5rouge119.29—Unverified
3Pegasus-CNN/DM (eng-it translation)rouge116.97—Unverified
4Pegasus-XSum (eng-it translation)rouge115.17—Unverified
#ModelMetricClaimedVerifiedStatus
1BART-ITROUGE-142.32—Unverified
2mT5ROUGE-140.6—Unverified
3mBARTROUGE-139.32—Unverified
4IT5-baseROUGE-137.98—Unverified
#ModelMetricClaimedVerifiedStatus
1PEGASUSROUGE-137.68—Unverified
2Multi-Stage Extractor/AbstractorROUGE-123.67—Unverified
#ModelMetricClaimedVerifiedStatus
1Two stage LLMsTest ROGUE-10.46—Unverified
#ModelMetricClaimedVerifiedStatus
1T2SAMROUGE48.5—Unverified
#ModelMetricClaimedVerifiedStatus
1mBARTMETEOR0.44—Unverified
#ModelMetricClaimedVerifiedStatus
1mBARTMETEOR0.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Two stage LLMsTest ROGUE-10.44—Unverified
#ModelMetricClaimedVerifiedStatus
1BertSumContent F129.8—Unverified