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
ACM -- Attribute Conditioning for Abstractive Multi Document Summarization0
A Method of Accounting Bigrams in Topic Models0
A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization0
A Multi-level Annotated Corpus of Scientific Papers for Scientific Document Summarization and Cross-document Relation Discovery0
Analysis of GraphSum's Attention Weights to Improve the Explainability of Multi-Document Summarization0
Automatically Determining a Proper Length for Multi-Document Summarization: A Bayesian Nonparametric Approach0
Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges0
Automatic Generation of Related Work Sections in Scientific Papers: An Optimization Approach0
Automatic Related Work Generation: A Meta Study0
Can one size fit all?: Measuring Failure in Multi-Document Summarization Domain Transfer0
Show:102550
← PrevPage 6 of 36Next →

No leaderboard results yet.