SOTAVerified

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 5160 of 359 papers

TitleStatusHype
Fair Summarization: Bridging Quality and Diversity in Extractive SummariesCode0
From Single to Multi: How LLMs Hallucinate in Multi-Document SummarizationCode0
Generating Wikipedia by Summarizing Long SequencesCode0
Auto-hMDS: Automatic Construction of a Large Heterogeneous Multilingual Multi-Document Summarization CorpusCode0
AllSummarizer system at MultiLing 2015: Multilingual single and multi-document summarizationCode0
Abstractive Multi-Document Summarization via Joint Learning with Single-Document SummarizationCode0
GLIMMER: Incorporating Graph and Lexical Features in Unsupervised Multi-Document SummarizationCode0
GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly ReviewsCode0
Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature SummarizationCode0
Error Analysis of using BART for Multi-Document Summarization: A Study for English and German LanguageCode0
Show:102550
← PrevPage 6 of 36Next →

No leaderboard results yet.