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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 31–40 of 359 papers

TitleStatusHype
AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document SummarizationCode1
Attribute First, then Generate: Locally-attributable Grounded Text GenerationCode1
CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information ManagementCode1
Data Augmentation for Abstractive Query-Focused Multi-Document SummarizationCode1
Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News ArticlesCode1
An Entity-Focused Approach to Generating Company Descriptions—0
An Assessment of the Accuracy of Automatic Evaluation in Summarization—0
3M:Multi-document Summarization Considering Main and Minor Relationship—0
Analyzing Stemming Approaches for Turkish Multi-Document Summarization—0
Analysis of GraphSum's Attention Weights to Improve the Explainability of Multi-Document Summarization—0
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