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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

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Showing 91–100 of 359 papers

TitleStatusHype
Detection of Topic and its Extrinsic Evaluation Through Multi-Document Summarization—0
Discriminative Marginalized Probabilistic Neural Method for Multi-Document Summarization of Medical Literature—0
A Spectral Method for Unsupervised Multi-Document Summarization—0
Document-aware Positional Encoding and Linguistic-guided Encoding for Abstractive Multi-document Summarization—0
Do Multi-Document Summarization Models Synthesize?—0
Drug Extraction from the Web: Summarizing Drug Experiences with Multi-Dimensional Topic Models—0
DualSum: a Topic-Model based approach for update summarization—0
AgreeSum: Agreement-Oriented Multi-Document Summarization—0
Combining State-of-the-Art Models with Maximal Marginal Relevance for Few-Shot and Zero-Shot Multi-Document Summarization—0
Coarse-to-Fine Query Focused Multi-Document Summarization—0
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