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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 6170 of 359 papers

TitleStatusHype
Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval0
SumREN: Summarizing Reported Speech about Events in NewsCode0
Combining State-of-the-Art Models with Maximal Marginal Relevance for Few-Shot and Zero-Shot Multi-Document Summarization0
Moving beyond word lists: towards abstractive topic labels for human-like topics of scientific documents0
How "Multi" is Multi-Document Summarization?Code0
Exploring the limits of a base BART for multi-document summarization in the medical domain0
Overview of the Third Workshop on Scholarly Document Processing0
Overview of MSLR2022: A Shared Task on Multi-document Summarization for Literature ReviewsCode0
An Extractive-Abstractive Approach for Multi-document Summarization of Scientific Articles for Literature ReviewCode0
Evaluating Pre-Trained Language Models on Multi-Document Summarization for Literature Reviews0
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