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 701–750 of 846 papers

TitleStatusHype
Forming Trees with Treeformers—0
ForumSum: A Multi-Speaker Conversation Summarization Dataset—0
Friendly Topic Assistant for Transformer Based Abstractive Summarization—0
From Extractive to Abstractive Summarization: A Journey—0
From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification—0
FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness—0
FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness—0
Frustratingly Easy Model Ensemble for Abstractive Summarization—0
Fully Abstractive Approach to Guided Summarization—0
Generating Categories for Sets of Entities—0
Generating Diverse and High-Quality Abstractive Summaries with Variational Transformers—0
Generating Formality-Tuned Summaries Using Input-Dependent Rewards—0
Generating Topic-Oriented Summaries Using Neural Attention—0
Generation of News Articles from Tweets : An Experiment—0
Generative Bridging Network for Neural Sequence Prediction—0
Generative Bridging Network in Neural Sequence Prediction—0
Gradient-Based Adversarial Factual Consistency Evaluation for Abstractive Summarization—0
Guide-to-Explain for Controllable Summarization—0
Guiding Generation for Abstractive Text Summarization Based on Key Information Guide Network—0
GUMsley: Evaluating Entity Salience in Summarization for 12 English Genres—0
GupShup: An Annotated Corpus for Abstractive Summarization of Open-Domain Code-Switched Conversations—0
Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization—0
HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation—0
How Private are Language Models in Abstractive Summarization?—0
How Ready are Pre-trained Abstractive Models and LLMs for Legal Case Judgement Summarization?—0
HydraSum - Disentangling Stylistic Features in Text Summarization using Multi-Decoder Models—0
IBMResearch at MEDIQA 2021: Toward Improving Factual Correctness of Radiology Report Abstractive Summarization—0
Improved Beam Search for Hallucination Mitigation in Abstractive Summarization—0
Improving Abstraction in Text Summarization—0
Improving Abstractive Summarization with Commonsense Knowledge—0
Improving Abstractive Text Summarization with History Aggregation—0
Improving Factual Consistency of Abstractive Summarization on Customer Feedback—0
Improving Factuality of Abstractive Summarization via Contrastive Reward Learning—0
Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection—0
Improving Faithfulness of Abstractive Summarization by Controlling Confounding Effect of Irrelevant Sentences—0
Improving generation quality of pointer networks via guided attention—0
Improving Long Text Understanding with Knowledge Distilled from Summarization Model—0
Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees—0
Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling—0
Improving Neural Abstractive Document Summarization with Structural Regularization—0
Improving Sequence-to-Sequence Learning via Optimal Transport—0
Improving Sequence-to-Sequence Models for Abstractive Text Summarization Using Meta Heuristic Approaches—0
Improving the Factual Accuracy of Abstractive Clinical Text Summarization using Multi-Objective Optimization—0
Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control—0
Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control—0
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise—0
Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation—0
Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization—0
Incorporating Question Answering-Based Signals into Abstractive Summarization via Salient Span Selection—0
Indian Legal Text Summarization: A Text Normalisation-based Approach—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