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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 331340 of 359 papers

TitleStatusHype
Extending a Single-Document Summarizer to Multi-Document: a Hierarchical Approach0
Extracting News Web Page Creation Time with DCTFinder0
Extractive Multi-document Summarization Using Multilayer Networks0
Extractive Multi Document Summarization using Dynamical Measurements of Complex Networks0
Extractive Multi-Document Summarization with Integer Linear Programming and Support Vector Regression0
Extractive Summarization by Aggregating Multiple Similarities0
Extractive Summarization: Limits, Compression, Generalized Model and Heuristics0
Extractive Summarization using Continuous Vector Space Models0
Extract with Order for Coherent Multi-Document Summarization0
Fast and Robust Compressive Summarization with Dual Decomposition and Multi-Task Learning0
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