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 351–359 of 359 papers

TitleStatusHype
Monolingual Distributional Similarity for Text-to-Text Generation—0
Multiple Aspect Summarization Using Integer Linear Programming—0
An Assessment of the Accuracy of Automatic Evaluation in Summarization—0
On-Demand Distributional Semantic Distance and Paraphrasing—0
Summarization of Historical Articles Using Temporal Event Clustering—0
On The Feasibility of Open Domain Referring Expression Generation Using Large Scale Folksonomies—0
Creation and use of Language Resources in a Question-Answering eHealth System—0
DualSum: a Topic-Model based approach for update summarization—0
Large-Margin Learning of Submodular Summarization Models—0
Show:102550
← PrevPage 8 of 8Next →

No leaderboard results yet.