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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 251260 of 359 papers

TitleStatusHype
Data-driven Paraphrasing and Stylistic Harmonization0
Sentence Similarity based on Dependency Tree Kernels for Multi-document Summarization0
TGSum: Build Tweet Guided Multi-Document Summarization Dataset0
Joint semantic discourse models for automatic multi-document summarization0
On Strategies of Human Multi-Document Summarization0
Measuring Semantic Similarity for Bengali Tweets Using WordNet0
Extractive Summarization by Aggregating Multiple Similarities0
AllSummarizer system at MultiLing 2015: Multilingual single and multi-document summarizationCode0
A Discursive Grid Approach to Model Local Coherence in Multi-document Summaries0
ExB Text Summarizer0
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