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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 121130 of 359 papers

TitleStatusHype
Neighborhood Rough Set based Multi-document Summarization0
BASS: Boosting Abstractive Summarization with Unified Semantic Graph0
Analysis of GraphSum's Attention Weights to Improve the Explainability of Multi-Document Summarization0
PoBRL: Optimizing Multi-Document Summarization by Blending Reinforcement Learning Policies0
Error Analysis of using BART for Multi-Document Summarization: A Study for English and German LanguageCode0
Identifying Helpful Sentences in Product Reviews0
Generating Related Work0
MS2: Multi-Document Summarization of Medical StudiesCode1
What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization0
Nutri-bullets: Summarizing Health Studies by Composing SegmentsCode0
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