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Abstractive Text Summarization
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
ARC: Argument Representation and Coverage Analysis for Zero-Shot Long Document Summarization with Instruction Following LLMs
Power-Law Decay Loss for Large Language Model Finetuning: Focusing on Information Sparsity to Enhance Generation Quality
Enhancing Abstractive Summarization of Scientific Papers Using Structure Information
Low-Resource Language Processing: An OCR-Driven Summarization and Translation Pipeline
ProdRev: A DNN framework for empowering customers using generative pre-trained transformers
A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting
GASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer Pharmacovigilance
AIstorian lets AI be a historian: A KG-powered multi-agent system for accurate biography generation
ARLED: Leveraging LED-based ARMAN Model for Abstractive Summarization of Persian Long Documents
A Hybrid Architecture with Efficient Fine Tuning for Abstractive Patent Document Summarization
Advancements in Natural Language Processing for Automatic Text Summarization
BRIDO: Bringing Democratic Order to Abstractive Summarization
CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarization
Beyond English: The Impact of Prompt Translation Strategies across Languages and Tasks in Multilingual LLMs
Self-Rationalization in the Wild: A Large Scale Out-of-Distribution Evaluation on NLI-related tasks
Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and Consistency
TReMu: Towards Neuro-Symbolic Temporal Reasoning for LLM-Agents with Memory in Multi-Session Dialogues
Can summarization approximate simplification? A gold standard comparison
Abstractive Text Summarization for Bangla Language Using NLP and Machine Learning Approaches
Abstractive Text Summarization for Contemporary Sanskrit Prose: Issues and Challenges
Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization
Temporal reasoning for timeline summarisation in social media
Survey of Pseudonymization, Abstractive Summarization & Spell Checker for Hindi and Marathi
Just What You Desire: Constrained Timeline Summarization with Self-Reflection for Enhanced Relevance
Survey on Abstractive Text Summarization: Dataset, Models, and Metrics
Length Controlled Generation for Black-box LLMs
How Private are Language Models in Abstractive Summarization?
DocSum: Domain-Adaptive Pre-training for Document Abstractive Summarization
Guide-to-Explain for Controllable Summarization
DTELS: Towards Dynamic Granularity of Timeline Summarization
Deceiving Question-Answering Models: A Hybrid Word-Level Adversarial Approach
Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning
Domain-specific Guided Summarization for Mental Health Posts
Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions
Optimizing the role of human evaluation in LLM-based spoken document summarization systems
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