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
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
LightPAL: Lightweight Passage Retrieval for Open Domain Multi-Document Summarization0
GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly ReviewsCode0
The Power of Summary-Source AlignmentsCode0
Text Generation: A Systematic Literature Review of Tasks, Evaluation, and ChallengesCode0
Which Information Matters? Dissecting Human-written Multi-document Summaries with Partial Information DecompositionCode0
Show:102550
← PrevPage 6 of 36Next →

No leaderboard results yet.