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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

Showing 341350 of 359 papers

TitleStatusHype
Fast Joint Compression and Summarization via Graph Cuts0
Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning0
Framework of Automatic Text Summarization Using Reinforcement Learning0
From TimeLines to StoryLines: A preliminary proposal for evaluating narratives0
Generating an Overview Report over Many Documents0
Generating Related Work0
Generating Supplementary Travel Guides from Social Media0
Graph-Based Approach to Recognizing CST Relations in Polish Texts0
Graph-based Neural Multi-Document Summarization0
Hierarchical Summarization: Scaling Up Multi-Document Summarization0
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