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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 201210 of 359 papers

TitleStatusHype
Generating Wikipedia by Summarizing Long SequencesCode0
Extractive Multi-document Summarization Using Multilayer Networks0
Taking into account Inter-sentence Similarity for Update Summarization0
Concept-Map-Based Multi-Document Summarization using Concept Coreference Resolution and Global Importance Optimization0
Towards Abstractive Multi-Document Summarization Using Submodular Function-Based Framework, Sentence Compression and Merging0
Abstractive Multi-document Summarization by Partial Tree Extraction, Recombination and Linearization0
Multi-Document Summarization using Distributed Bag-of-Words Model0
Affinity-Preserving Random Walk for Multi-Document Summarization0
Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization0
Summarizing World Speak : A Preliminary Graph Based Approach0
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