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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 1120 of 359 papers

TitleStatusHype
EventSum: A Large-Scale Event-Centric Summarization Dataset for Chinese Multi-News Documents0
Coverage-based Fairness in Multi-document SummarizationCode0
Fair Summarization: Bridging Quality and Diversity in Extractive SummariesCode0
From Single to Multi: How LLMs Hallucinate in Multi-Document SummarizationCode0
GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News SummarizationCode0
Leveraging Long-Context Large Language Models for Multi-Document Understanding and Summarization in Enterprise Applications0
BERT-VBD: Vietnamese Multi-Document Summarization Framework0
GLIMMER: Incorporating Graph and Lexical Features in Unsupervised Multi-Document SummarizationCode0
SumRecom: A Personalized Summarization Approach by Learning from Users' Feedback0
Rethinking Transformer-based Multi-document Summarization: An Empirical Investigation0
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