Papers Extractive Document Summarization
“Extractive Document Summarization” 태그가 달린 논문 29편 · 필터 해제
GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state
Extracting summaries from long documents can be regarded as sentence classification using the structural information of the documents. How to use such structural information to summarize a document is challenging. In thi…
ArticlesDocument SummarizationExtractive Document SummarizationExtractive Summarization+6HeterGraphLongSum: Heterogeneous Graph Neural Network with Passage Aggregation for Extractive Long Document Summarization
Graph Neural Network (GNN)-based models have proven effective in various Natural Language Processing (NLP) tasks in recent years. Specifically, in the case of the Extractive Document Summarization (EDS) task, modeling do…
Document SummarizationExtractive Document SummarizationGraph Neural NetworkSentenceGUSUM: Graph-based Unsupervised Summarization Using Sentence Features Scoring and Sentence-BERT
Unsupervised extractive document summarization aims to extract salient sentences from a document without requiring a labelled corpus. In existing graph-based methods, vertex and edge weights are usually created by calcul…
Document SummarizationExtractive Document SummarizationExtractive Text SummarizationGraph Ranking+5Sparse Optimization for Unsupervised Extractive Summarization of Long Documents with the Frank-Wolfe Algorithm
We address the problem of unsupervised extractive document summarization, especially for long documents. We model the unsupervised problem as a sparse auto-regression one and approximate the resulting combinatorial probl…
Document SummarizationExtractive Document SummarizationExtractive Summarizationregression+4DiMSum: Distributed and Multilingual Summarization of Financial Narratives
This paper was submitted for Financial Narrative Summarization (FNS) task in FNP-2022 workshop. The objective of the task was to generate not more than 1000 words summaries for the annual financial reports written in Eng…
Document AIDocument SummarizationExtractive Document SummarizationAn Exploitation of Heterogeneous Graph Neural Network for Extractive Long Document Summarization
Heterogeneous Graph Neural Networks (HeterGNN) has been recently introduced as an emergent approach for many Natural Language Processing (NLP) tasks by enriching the complex information between word and sentence. In this…
Document SummarizationExtractive Document SummarizationGraph AttentionGraph Neural Network+1GUSUM: Graph-Based Unsupervised Summarization using Sentence-BERT and Sentence Features
Unsupervised extractive document summarization aims to extract salient sentences from a document without requiring a labelled corpus. In existing graph-based methods, vertex and edge weights are mostly created by calcula…
Document SummarizationExtractive Document SummarizationExtractive Text SummarizationGraph Ranking+5Investigating Entropy for Extractive Document Summarization
Automatic text summarization aims to cut down readers time and cognitive effort by reducing the content of a text document without compromising on its essence. Ergo, informativeness is the prime attribute of document sum…
AttributeDocument SummarizationExtractive Document SummarizationInformativeness+3Centrality Meets Centroid: A Graph-based Approach for Unsupervised Document Summarization
Unsupervised document summarization has re-acquired lots of attention in recent years thanks to its simplicity and data independence. In this paper, we propose a graph-based unsupervised approach for extractive document …
Document SummarizationExtractive Document SummarizationSentenceUnsupervised Extractive Summarization by Pre-training Hierarchical Transformers
Unsupervised extractive document summarization aims to select important sentences from a document without using labeled summaries during training. Existing methods are mostly graph-based with sentences as nodes and edge …
Document SummarizationExtractive Document SummarizationExtractive SummarizationExtractive Text Summarization+2Heterogeneous Graph Neural Networks for Extractive Document Summarization
As a crucial step in extractive document summarization, learning cross-sentence relations has been explored by a plethora of approaches. An intuitive way is to put them in the graph-based neural network, which has a more…
Document SummarizationExtractive Document SummarizationExtractive SummarizationExtractive Text Summarization+1Combining Word Embeddings and N-grams for Unsupervised Document Summarization
Graph-based extractive document summarization relies on the quality of the sentence similarity graph. Bag-of-words or tf-idf based sentence similarity uses exact word matching, but fails to measure the semantic similarit…
DiversityDocument SummarizationExtractive Document SummarizationExtractive Summarization+8AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization
Redundancy-aware extractive summarization systems score the redundancy of the sentences to be included in a summary either jointly with their salience information or separately as an additional sentence scoring step. Pre…
DiversityDocument SummarizationExtractive Document SummarizationExtractive Summarization+2At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization
Extractive methods have been proven effective in automatic document summarization. Previous works perform this task by identifying informative contents at sentence level. However, it is unclear whether performing extract…
Constituency ParsingDocument SummarizationExtractive Document SummarizationExtractive Summarization+2Text Summarization with Pretrained Encoders
Bidirectional Encoder Representations from Transformers (BERT) represents the latest incarnation of pretrained language models which have recently advanced a wide range of natural language processing tasks. In this paper…
Abstractive Text SummarizationDecoderDocument SummarizationExtractive Document Summarization+3Learning with fuzzy hypergraphs: a topical approach to query-oriented text summarization
Existing graph-based methods for extractive document summarization represent sentences of a corpus as the nodes of a graph or a hypergraph in which edges depict relationships of lexical similarity between sentences. Such…
Document SummarizationExtractive Document SummarizationExtractive Text SummarizationSentence+1Fine-tune BERT for Extractive Summarization
BERT, a pre-trained Transformer model, has achieved ground-breaking performance on multiple NLP tasks. In this paper, we describe BERTSUM, a simple variant of BERT, for extractive summarization. Our system is the state o…
Extractive Document SummarizationExtractive SummarizationExtractive Text SummarizationLanguage Model Pre-training for Hierarchical Document Representations
Hierarchical neural architectures are often used to capture long-distance dependencies and have been applied to many document-level tasks such as summarization, document segmentation, and sentiment analysis. However, eff…
Document SummarizationExtractive Document SummarizationExtractive Text SummarizationLanguage Modeling+5DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization
We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization. Given any document-summary pair, we estimate a salience score, which is modeled using an attention-b…
Contrastive LearningDocument SummarizationExtractive Document SummarizationExtractive Text SummarizationNeural Latent Extractive Document Summarization
Extractive summarization models require sentence-level labels, which are usually created heuristically (e.g., with rule-based methods) given that most summarization datasets only have document-summary pairs. Since these …
Document SummarizationExtractive Document SummarizationExtractive SummarizationExtractive Text Summarization+1