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Text Summarization

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하위 태스크: 12
평가표: 37
보존된 평가 기록: 233

Text Summarization is a natural language processing (NLP) task that involves condensing a lengthy text document into a shorter, more compact version while still retaining the most important information and meaning. The goal is to produce a summary that accurately represents the content of the original text in a concise form.

There are different approaches to text summarization, including extractive methods that identify and extract important sentences or phrases from the text, and abstractive methods that generate new text based on the content of the original text.

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Text Summarization

논문

APE: Selective Fine-tuning with Acceptance Criteria for Language Model Adaptation

LRCTI: A Large Language Model-Based Framework for Multi-Step Evidence Retrieval and Reasoning in Cyber Threat Intelligence Credibility Verification

Show, Tell and Summarize: Dense Video Captioning Using Visual Cue Aided Sentence Summarization

Dense Video Captioning using Graph-based Sentence Summarization

GenerationPrograms: Fine-grained Attribution with Executable Programs

Arctic Long Sequence Training: Scalable And Efficient Training For Multi-Million Token Sequences

On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention

Aspect-Based Opinion Summarization with Argumentation Schemes

Improving Fairness of Large Language Models in Multi-document Summarization

Improving large language models with concept-aware fine-tuning

Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs

NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization

ARC: Argument Representation and Coverage Analysis for Zero-Shot Long Document Summarization with Instruction Following LLMs

MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection

FiLLM -- A Filipino-optimized Large Language Model based on Southeast Asia Large Language Model (SEALLM)

Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal Learning

Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature Summarization

Power-Law Decay Loss for Large Language Model Finetuning: Focusing on Information Sparsity to Enhance Generation Quality

Hallucinate at the Last in Long Response Generation: A Case Study on Long Document Summarization

Enhancing Abstractive Summarization of Scientific Papers Using Structure Information

A Structured Literature Review on Traditional Approaches in Current Natural Language Processing

Low-Resource Language Processing: An OCR-Driven Summarization and Translation Pipeline

ProdRev: A DNN framework for empowering customers using generative pre-trained transformers

Multimodal Survival Modeling in the Age of Foundation Models

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting

Integrating Video and Text: A Balanced Approach to Multimodal Summary Generation and Evaluation

Document Attribution: Examining Citation Relationships using Large Language Models

GASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer Pharmacovigilance

SEval-Ex: A Statement-Level Framework for Explainable Summarization Evaluation

OODTE: A Differential Testing Engine for the ONNX Optimizer

Multi-agents based User Values Mining for Recommendation

PipeSpec: Breaking Stage Dependencies in Hierarchical LLM Decoding

Explanatory Summarization with Discourse-Driven Planning

ConTextual: Improving Clinical Text Summarization in LLMs with Context-preserving Token Filtering and Knowledge Graphs

A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization

Automatic Text Summarization (ATS) for Research Documents in Sorani Kurdish