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Document Summarization
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Automatic Document Summarization is the task of rewriting a document into its shorter form while still retaining its important content. The most popular two paradigms are extractive approaches and abstractive approaches. Extractive approaches generate summaries by extracting parts of the original document (usually sentences), while abstractive methods may generate new words or phrases which are not in the original document.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Arctic Long Sequence Training: Scalable And Efficient Training For Multi-Million Token Sequences
Improving Fairness of Large Language Models in Multi-document Summarization
ARC: Argument Representation and Coverage Analysis for Zero-Shot Long Document Summarization with Instruction Following LLMs
Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature Summarization
Hallucinate at the Last in Long Response Generation: A Case Study on Long Document Summarization
Document Attribution: Examining Citation Relationships using Large Language Models
A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization
Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document Summarization
Align to Structure: Aligning Large Language Models with Structural Information
M-DocSum: Do LVLMs Genuinely Comprehend Interleaved Image-Text in Document Summarization?
Can one size fit all?: Measuring Failure in Multi-Document Summarization Domain Transfer
UniHDSA: A Unified Relation Prediction Approach for Hierarchical Document Structure Analysis
Agent-Enhanced Large Language Models for Researching Political Institutions
A Hybrid Architecture with Efficient Fine Tuning for Abstractive Patent Document Summarization
Mitigating Preference Hacking in Policy Optimization with Pessimism
Multi2: Multi-Agent Test-Time Scalable Framework for Multi-Document Processing
LAG: LLM agents for Leaderboard Auto Generation on Demanding
LM Agents for Coordinating Multi-User Information Gathering
Exploring Synaptic Resonance in Large Language Models: A Novel Approach to Contextual Memory Integration
Discourse-Driven Evaluation: Unveiling Factual Inconsistency in Long Document Summarization
Scaling Multi-Document Event Summarization: Evaluating Compression vs. Full-Text Approaches
HyGen: Efficient LLM Serving via Elastic Online-Offline Request Co-location
Progressive Document-level Text Simplification via Large Language Models
End-to-End Long Document Summarization using Gradient Caching
A Rhetorical Relations-Based Framework for Tailored Multimedia Document Summarization
EventSum: A Large-Scale Event-Centric Summarization Dataset for Chinese Multi-News Documents
Precise Length Control in Large Language Models
Coverage-based Fairness in Multi-document Summarization
Mitigating Knowledge Conflicts in Language Model-Driven Question Answering
Fair Summarization: Bridging Quality and Diversity in Extractive Summaries
What is Wrong with Perplexity for Long-context Language Modeling?
Hybrid Deep Learning for Legal Text Analysis: Predicting Punishment Durations in Indonesian Court Rulings
Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges
Optimizing the role of human evaluation in LLM-based spoken document summarization systems
DiscoGraMS: Enhancing Movie Screen-Play Summarization using Movie Character-Aware Discourse Graph
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