Multi-Document Summarization
93 papers with code • 5 benchmarks • 15 datasets
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
Benchmarks
These leaderboards are used to track progress in Multi-Document Summarization
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Datasets
Multi-News
WikiSum
WCEP
DUC 2004
aquamuse
OpoSum
MATINF
MS^2
Most implemented papers
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization
ucfnlp/multidoc_summarization
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EMNLP 2018
Generating a text abstract from a set of documents remains a challenging task.
A Temporally Sensitive Submodularity Framework for Timeline Summarization
Timeline summarization (TLS) creates an overview of long-running events via dated daily summaries for the most important dates.
Hierarchical Transformers for Multi-Document Summarization
In this paper, we develop a neural summarization model which can effectively process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner.
Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization
ucfnlp/summarization-dpp-capsnet
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ACL 2019
The most important obstacles facing multi-document summarization include excessive redundancy in source descriptions and the looming shortage of training data.
Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization
Concept map-based multi-document summarization has recently been proposed as a variant of the traditional summarization task with graph-structured summaries.
Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model
Alex-Fabbri/Multi-News
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ACL 2019
Automatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly.
Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs
denisewong1/ASX300
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IJCNLP 2019
Query-based open-domain NLP tasks require information synthesis from long and diverse web results.
Extractive Multi-document Summarization using K-means, Centroid-based Method, MMR, and Sentence Position
Multi-document summarization is more challenging than single-document summarization since it has to solve the problem of overlapping information among sentences from different documents.
