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Multi-Document Summarization

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

Papers

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Showing 151–175 of 359 papers

TitleStatusHype
Graph-Based Approach to Recognizing CST Relations in Polish Texts—0
Graph-based Neural Multi-Document Summarization—0
Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization—0
Hierarchical Summarization: Scaling Up Multi-Document Summarization—0
Complex Question Answering: Unsupervised Learning Approaches and Experiments—0
Highlight-Transformer: Leveraging Key Phrase Aware Attention to Improve Abstractive Multi-Document Summarization—0
A Preliminary Study of Tweet Summarization using Information Extraction—0
A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization—0
Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning—0
Affinity-Preserving Random Walk for Multi-Document Summarization—0
Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees—0
Learning to Generate Coherent Summary with Discriminative Hidden Semi-Markov Model—0
Leveraging Attribute Conditioning for Abstractive Multi Document Summarization—0
Improving the Estimation of Word Importance for News Multi-Document Summarization—0
Improving Update Summarization via Supervised ILP and Sentence Reranking—0
Dependency Structure for News Document Summarization—0
Int\'egration de la similarit\'e entre phrases comme crit\`ere pour le r\'esum\'e multi-document (Integrating sentence similarity as a constraint for multi-document summarization)—0
Interactive Abstractive Summarization for Event News Tweets—0
基于实体信息增强及多粒度融合的多文档摘要(Multi-Document Summarization Based on Entity Information Enhancement and Multi-Granularity Fusion)—0
Joint Lifelong Topic Model and Manifold Ranking for Document Summarization—0
Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback—0
Joint semantic discourse models for automatic multi-document summarization—0
Fast Joint Compression and Summarization via Graph Cuts—0
LAG: LLM agents for Leaderboard Auto Generation on Demanding—0
A novel extractive multi-document text summarization system using quantum-inspired genetic algorithm: MTSQIGA—0
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