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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 301–310 of 359 papers

TitleStatusHype
Combining State-of-the-Art Models with Maximal Marginal Relevance for Few-Shot and Zero-Shot Multi-Document Summarization—0
Complex Question Answering: Unsupervised Learning Approaches and Experiments—0
Concept-Map-Based Multi-Document Summarization using Concept Coreference Resolution and Global Importance Optimization—0
Content based Weighted Consensus Summarization—0
Controllable Multi-document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards—0
Creation and use of Language Resources in a Question-Answering eHealth System—0
Data-driven Paraphrasing and Stylistic Harmonization—0
Deconstructing Human Literature Reviews -- A Framework for Multi-Document Summarization—0
Deep Attentive Sentence Ordering Network—0
Detection of Topic and its Extrinsic Evaluation Through Multi-Document Summarization—0
Show:102550
← PrevPage 31 of 36Next →

No leaderboard results yet.