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 151160 of 359 papers

TitleStatusHype
Summary-Source Proposition-level Alignment: Task, Datasets and Supervised BaselineCode1
Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document SummarizationCode1
Massive Multi-Document Summarization of Product Reviews with Weak Supervision0
SummPip: Unsupervised Multi-Document Summarization with Sentence Graph CompressionCode1
Multi-Granularity Interaction Network for Extractive and Abstractive Multi-Document Summarization0
Pre-training via ParaphrasingCode1
DynE: Dynamic Ensemble Decoding for Multi-Document SummarizationCode1
Read what you need: Controllable Aspect-based Opinion Summarization of Tourist ReviewsCode0
Leveraging Graph to Improve Abstractive Multi-Document SummarizationCode0
A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events PortalCode1
Show:102550
← PrevPage 16 of 36Next →

No leaderboard results yet.