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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 161170 of 359 papers

TitleStatusHype
SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document SummarizationCode1
CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information ManagementCode1
A Multi-level Annotated Corpus of Scientific Papers for Scientific Document Summarization and Cross-document Relation Discovery0
Neural Abstractive Summarization with Structural Attention0
Query Focused Multi-Document Summarization with Distant Supervision0
GameWikiSum: a Novel Large Multi-Document Summarization DatasetCode1
Rough Set based Aggregate Rank Measure & its Application to Supervised Multi Document Summarization0
Extractive Multi-document Summarization using K-means, Centroid-based Method, MMR, and Sentence PositionCode0
Subtopic-driven Multi-Document Summarization0
Unsupervised Aspect-Based Multi-Document Abstractive Summarization0
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