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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 151175 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
A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events PortalCode1
Leveraging Graph to Improve Abstractive Multi-Document SummarizationCode0
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
Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations0
Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document InputsCode0
Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization0
Generating an Overview Report over Many Documents0
Joint Lifelong Topic Model and Manifold Ranking for Document Summarization0
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