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Multi-Document Summarization
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Multi-Document Summarization is a process of representing a set of documents with a short piece of text by capturing the relevant information and filtering out the redundant information. Two prominent approaches to Multi-Document Summarization are extractive and abstractive summarization. Extractive summarization systems aim to extract salient snippets, sentences or passages from documents, while abstractive summarization systems aim to concisely paraphrase the content of the documents.
Source: Multi-Document Summarization using Distributed Bag-of-Words Model
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Improving Fairness of Large Language Models in Multi-document Summarization
Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature Summarization
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
Can one size fit all?: Measuring Failure in Multi-Document Summarization Domain Transfer
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
Scaling Multi-Document Event Summarization: Evaluating Compression vs. Full-Text Approaches
EventSum: A Large-Scale Event-Centric Summarization Dataset for Chinese Multi-News Documents
Coverage-based Fairness in Multi-document Summarization
Fair Summarization: Bridging Quality and Diversity in Extractive Summaries
From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization
GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization
Leveraging Long-Context Large Language Models for Multi-Document Understanding and Summarization in Enterprise Applications
BERT-VBD: Vietnamese Multi-Document Summarization Framework
GLIMMER: Incorporating Graph and Lexical Features in Unsupervised Multi-Document Summarization
SumRecom: A Personalized Summarization Approach by Learning from Users' Feedback
Rethinking Transformer-based Multi-document Summarization: An Empirical Investigation
LightPAL: Lightweight Passage Retrieval for Open Domain Multi-Document Summarization
GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews
The Power of Summary-Source Alignments
Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges
Which Information Matters? Dissecting Human-written Multi-document Summaries with Partial Information Decomposition
Disentangling Specificity for Abstractive Multi-document Summarization
Understanding Position Bias Effects on Fairness in Social Multi-Document Summarization
OARelatedWork: A Large-Scale Dataset of Related Work Sections with Full-texts from Open Access Sources
Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotation
Attribute First, then Generate: Locally-attributable Grounded Text Generation
NewsQs: Multi-Source Question Generation for the Inquiring Mind
SKT5SciSumm -- Revisiting Extractive-Generative Approach for Multi-Document Scientific Summarization
Benchmarking LLMs on the Semantic Overlap Summarization Task
Shaping Political Discourse using multi-source News Summarization
OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization
Supervising the Centroid Baseline for Extractive Multi-Document Summarization
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