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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 221230 of 359 papers

TitleStatusHype
Assessing the performance of Olelo, a real-time biomedical question answering application0
Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback0
Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization0
Graph-based Neural Multi-Document Summarization0
Extract with Order for Coherent Multi-Document Summarization0
Extractive Summarization: Limits, Compression, Generalized Model and Heuristics0
Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept MapsCode0
Centroid-based Text Summarization through Compositionality of Word EmbeddingsCode0
Utilizing Automatic Predicate-Argument Analysis for Concept Map Mining0
Exploring Text Links for Coherent Multi-Document Summarization0
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