Papers Extractive Text Summarization
“Extractive Text Summarization” 태그가 달린 논문 96편 · 필터 해제
Heterogeneous 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+8Extractive Summarization as Text Matching
This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems. Instead of following the commonly used framework of extracting sentences individually and modeling the relation…
Document SummarizationExtractive SummarizationExtractive Text SummarizationSemantic Text Matching+3Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model
Online social media platforms are turning into the prime source of news and narratives about worldwide events. However,a systematic summarization-based narrative extraction that can facilitate communicating the main unde…
Extractive Text SummarizationmodelText SummarizationAREDSUM: 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+2Hybrid MemNet for Extractive Summarization
Extractive text summarization has been an extensive research problem in the field of natural language understanding. While the conventional approaches rely mostly on manually compiled features to generate the summary, fe…
Document SummarizationExtractive SummarizationExtractive Text SummarizationNatural Language Understanding+1Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization
Automated multi-document extractive text summarization is a widely studied research problem in the field of natural language understanding. Such extractive mechanisms compute in some form the worthiness of a sentence to …
DiversityExtractive SummarizationExtractive Text SummarizationNatural Language Understanding+3Extractive 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. Also, since multi-document summar…
ClusteringDocument SummarizationExtractive Text SummarizationMulti-Document Summarization+2Towards Supervised Extractive Text Summarization via RNN-based Sequence Classification
This article briefly explains our submitted approach to the DocEng'19 competition on extractive summarization. We implemented a recurrent neural network based model that learns to classify whether an article's sentence b…
ClassificationExtractive SummarizationExtractive Text SummarizationGeneral Classification+2Towards automatic extractive text summarization of A-133 Single Audit reports with machine learning
The rapid growth of text data has motivated the development of machine-learning based automatic text summarization strategies that concisely capture the essential ideas in a larger text. This study aimed to devise an ext…
BIG-bench Machine LearningClusteringExtractive SummarizationExtractive Text Summarization+3Reading Like HER: Human Reading Inspired Extractive Summarization
In this work, we re-examine the problem of extractive text summarization for long documents. We observe that the process of extracting summarization of human can be divided into two stages: 1) a rough reading stage to lo…
Decision MakingExtractive SummarizationExtractive Text SummarizationText SummarizationDiscourse-Aware Neural Extractive Text Summarization
Recently BERT has been adopted for document encoding in state-of-the-art text summarization models. However, sentence-based extractive models often result in redundant or uninformative phrases in the extracted summaries.…
Extractive Text SummarizationSentenceText SummarizationSummary Level Training of Sentence Rewriting for Abstractive Summarization
As an attempt to combine extractive and abstractive summarization, Sentence Rewriting models adopt the strategy of extracting salient sentences from a document first and then paraphrasing the selected ones to generate a …
Abstractive Text SummarizationExtractive Text SummarizationNatural Language UnderstandingReinforcement Learning+2A topic-based sentence representation for extractive text summarization
In this study, we examine the effect of probabilistic topic model-based word representations, on sentence-based extractive summarization. We formulate the task of summary extraction as a binary classification problem, an…
BIG-bench Machine LearningBinary ClassificationExtractive SummarizationExtractive Text Summarization+2A study of semantic augmentation of word embeddings for extractive summarization
In this study we examine the effect of semantic augmentation approaches on extractive text summarization. Wordnet hypernym relations are used to extract term-frequency concept information, subsequently concatenated to se…
ClusteringDimensionality ReductionExtractive SummarizationExtractive Text Summarization+3Exploring Domain Shift in Extractive Text Summarization
Although domain shift has been well explored in many NLP applications, it still has received little attention in the domain of extractive text summarization. As a result, the model is under-utilizing the nature of the tr…
Extractive Text SummarizationMeta-LearningText SummarizationText 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+3STRASS: A Light and Effective Method for Extractive Summarization Based on Sentence Embeddings
This paper introduces STRASS: Summarization by TRAnsformation Selection and Scoring. It is an extractive text summarization method which leverages the semantic information in existing sentence embedding spaces. Our metho…
CPUDocument EmbeddingExtractive SummarizationExtractive Text Summarization+5Searching for Effective Neural Extractive Summarization: What Works and What's Next
The recent years have seen remarkable success in the use of deep neural networks on text summarization. However, there is no clear understanding of \textit{why} they perform so well, or \textit{how} they might be improve…
Extractive SummarizationExtractive Text SummarizationText Summarization