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Multi-Document Summarization

13 papers with code ยท Natural Language Processing
Subtask of Text Summarization

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GameWikiSum: a Novel Large Multi-Document Summarization Dataset

17 Feb 2020

In this paper, we propose GameWikiSum, a new domain-specific dataset for multi-document summarization, which is one hundred times larger than commonly used datasets, and in another domain than news.

DOCUMENT SUMMARIZATION MULTI-DOCUMENT SUMMARIZATION

Rough Set based Aggregate Rank Measure & its Application to Supervised Multi Document Summarization

9 Feb 2020

Firstly, it proposes a Rough Set based measure to be utilized for numerical characterization of within class ranking of objects.

DOCUMENT SUMMARIZATION MULTI-DOCUMENT SUMMARIZATION

Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization

WS 2019

To overcome these limitations, we present a novel method, which makes use of two types of sentence embeddings: universal embeddings, which are trained on a large unrelated corpus, and domain-specific embeddings, which are learned during training.

DOCUMENT SUMMARIZATION MULTI-DOCUMENT SUMMARIZATION SENTENCE EMBEDDINGS

Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations

WS 2019

Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select a most probable set of summary sentences according to a probabilistic measure defined by respectively modeling sentence prominence and pairwise repulsion.

DOCUMENT SUMMARIZATION MULTI-DOCUMENT SUMMARIZATION POINT PROCESSES

Subtopic-driven Multi-Document Summarization

IJCNLP 2019

In multi-document summarization, a set of documents to be summarized is assumed to be on the same topic, known as the underlying topic in this paper.

DOCUMENT SUMMARIZATION MULTI-DOCUMENT SUMMARIZATION

Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations

WS 2019

Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select the most probable set of sentences to form a summary according to a probability measure defined by modeling sentence prominence and pairwise repulsion.

DOCUMENT SUMMARIZATION MULTI-DOCUMENT SUMMARIZATION POINT PROCESSES

Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization

WS 2019

To overcome these limitations, we present a novel method, which makes use of two types of sentence embeddings: universal embeddings, which are trained on a large unrelated corpus, and domain-specific embeddings, which are learned during training.

DOCUMENT SUMMARIZATION MULTI-DOCUMENT SUMMARIZATION SENTENCE EMBEDDINGS

Generating an Overview Report over Many Documents

17 Aug 2019

To overcome this obstacle, we present NDORGS (Numerous Documents' Overview Report Generation Scheme) that integrates text filtering, keyword scoring, single-document summarization (SDS), topic modeling, MDS, and title generation to generate a coherent, well-structured ORPT.

DECISION MAKING DOCUMENT SUMMARIZATION MULTI-DOCUMENT SUMMARIZATION