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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 126–150 of 359 papers

TitleStatusHype
Extractive Multi-Document Summarization with Integer Linear Programming and Support Vector Regression—0
Interactive Abstractive Summarization for Event News Tweets—0
Improving Update Summarization via Supervised ILP and Sentence Reranking—0
Improving the Estimation of Word Importance for News Multi-Document Summarization—0
Dependency Structure for News Document Summarization—0
Abstractive News Summarization based on Event Semantic Link Network—0
Ant Colony System for Multi-Document Summarization—0
Improving Multi-Document Summarization via Text Classification—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
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
Generating an Overview Report over Many Documents—0
Answering Questions from Multiple Documents -- the Role of Multi-Document Summarization—0
From TimeLines to StoryLines: A preliminary proposal for evaluating narratives—0
Can one size fit all?: Measuring Failure in Multi-Document Summarization Domain Transfer—0
Identifying Helpful Sentences in Product Reviews—0
Framework of Automatic Text Summarization Using Reinforcement Learning—0
Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization—0
Highlight-Transformer: Leveraging Key Phrase Aware Attention to Improve Abstractive Multi-Document Summarization—0
Generating Related Work—0
Generating Supplementary Travel Guides from Social Media—0
CIST System for CL-SciSumm 2016 Shared Task—0
CIST System Report for ACL MultiLing 2013 -- Track 1: Multilingual Multi-document Summarization—0
Absformer: Transformer-based Model for Unsupervised Multi-Document Abstractive Summarization—0
An Unsupervised Multi-Document Summarization Framework Based on Neural Document Model—0
A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization—0
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