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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 3140 of 359 papers

TitleStatusHype
A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events PortalCode1
SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document SummarizationCode1
CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information ManagementCode1
GameWikiSum: a Novel Large Multi-Document Summarization DatasetCode1
Bottom-Up Abstractive SummarizationCode1
GenerationPrograms: Fine-grained Attribution with Executable ProgramsCode0
Improving Fairness of Large Language Models in Multi-document SummarizationCode0
Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature SummarizationCode0
A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization0
Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document SummarizationCode0
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