Document Summarization

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

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Latest papers without code

Learning Syntactic and Dynamic Selective Encoding for Document Summarization

25 Mar 2020

Our approach has the following contributions: first, we incorporate syntactic information such as constituency parsing trees into the encoding sequence to learn both the semantic and syntactic information from the document, resulting in more accurate summary; second, we propose a dynamic gate network to select the salient information based on the context of the decoder state, which is essential to document summarization.

CONSTITUENCY PARSING DOCUMENT SUMMARIZATION

Selective Attention Encoders by Syntactic Graph Convolutional Networks for Document Summarization

18 Mar 2020

Abstractive text summarization is a challenging task, and one need to design a mechanism to effectively extract salient information from the source text and then generate a summary.

ABSTRACTIVE TEXT SUMMARIZATION DOCUMENT SUMMARIZATION

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

Hybrid MemNet for Extractive Summarization

25 Dec 2019

Extractive text summarization has been an extensive research problem in the field of natural language understanding.

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

An Editorial Network for Enhanced Document Summarization

WS 2019

We suggest a new idea of Editorial Network {--} a mixed extractive-abstractive summarization approach, which is applied as a post-processing step over a given sequence of extracted sentences.

ABSTRACTIVE TEXT SUMMARIZATION 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 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