[PDF] Top 20 Extractive Text Summarization with Deep Learning
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Extractive Text Summarization with Deep Learning
... “Textual information in the form of digital documents quickly accumulates to huge amounts of data. Most of this large volume of documents is unstructured: it is unrestricted and has not been organized into traditional ... See full document
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LeafNATS: An Open Source Toolkit and Live Demo System for Neural Abstractive Text Summarization
... abstractive text summa- rization (NATS) has gained a lot of popular- ity (Rush et ...from extractive text sum- marization (Gambhir and Gupta, 2017; Nallapati et ...modern deep learning ... See full document
6
Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization
... of text along with natural language generation systems (based on information items, predicate arguments and semantic ...Recently, deep learning ar- chitectures have been widely adopted in abstrac- ... See full document
11
Self Supervised Learning for Contextualized Extractive Summarization
... In this paper, we propose three self-supervised tasks (Mask, Replace and Switch), where the model is required to learn the document-level structure and context. The knowledge learned about the document during the ... See full document
7
Ranking Sentences for Extractive Summarization with Reinforcement Learning
... Recent deep learning methods (K˚ageb¨ack et ...the learning objective and the evaluation crite- rion ...forcement learning objective: sentences are highly ranked if they occur in highly ... See full document
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A topic based sentence representation for extractive text summarization
... pervised learning procedure; it is necessary to have a set of ground truth sentences, that is, sen- tences that are indeed valid summaries of input ...the extractive model is ...cilitate learning ... See full document
9
Automatic Amharic Text Summarization using NLP Parser
... a text document of extractive query oriented single document by using deep auto-encoder and compute feature space from its term frequency ...input text document and selects the most important ... See full document
7
Extractive Summarization Using Supervised and Semi Supervised Learning
... supervised learning is very time ...as text classification (Denis and Gilleron, 2003), noun phrase chunk- ing (Pierce and Cardie, 2001), parsing (Sarkar, 2001) and reference or relation resolution (Mul- ler ... See full document
8
Iterative Document Representation Learning Towards Summarization with Polishing
... abstractive summarization, extractive summarization remains an attractive method as it is capable of generating more gram- matically and semantically correct ...tractive summarization, Cheng ... See full document
10
Extractive Summarization Using Multi Task Learning with Document Classification
... our summarization approach with document classifi- ...the text collection has explicitly annotated document labels, which in- cludes the collection of news articles with their category information, product ... See full document
10
DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization
... Abstractive. A vast majority of abstractive summariz- ers are built based on the encoder-decoder structure. (See, Liu, and Manning 2017) incorporates a pointing mecha- nism into the encoder-decoder, such that their model ... See full document
8
Complete Pre Processing Phase of Punjabi Text Extractive Summarization System
... Mohamed Abdel Fattah and Fuji Ren (2008). Automatic Text Summarization. In: Proceedings of World Academy of Science Engineering and Technology, volume 27, pages 192-195. Joel L. Neto, (2000). Document ... See full document
8
Content Selection in Deep Learning Models of Summarization
... with deep learning models of summarization across the domains of news, personal stories, meetings, and medi- cal articles in order to understand how content selection is ...art extractive ... See full document
15
Deep Recurrent Generative Decoder for Abstractive Text Summarization
... Some research works employ topic models to capture the latent information from source docu- ments or sentences. Wang et al. (2009) proposed a new Bayesian sentence-based topic model by making use of both the ... See full document
10
Extractive Based Automatic Text Summarization
... in text summarization is efficient because these relations are easily identifiable within the source text, and vast knowledge bases are not necessary for ...than deep semantic ... See full document
14
Survey Paper on Text Summarization Methods
... Abstractive Summarization works by generating a sentence from data having semantic representation and after that generate a summary by using natural language processing (NLP) ...require deep knowledge of ... See full document
6
Neural Extractive Text Summarization with Syntactic Compression
... single-document summarization based on joint extraction and syntactic ...For learning, we construct or- acle extractive-compressive summaries, then learn both of our components jointly with this ... See full document
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Automatic Punjabi Text Extractive Summarization System
... Automatic text summarization (Kyoomarsi et ...source text into a shorter form retaining its information content and overall ...meaning. Text Summarization (Lin, 2009) Process can be ... See full document
8
Bayesian Learning in Text Summarization
... Tables 2 through 4 show how the Bayesian sum- marist performs on G1K3, G2K3, and G3K3. The tables list results in precision at compression rates (r) of interest (0 < r < 1). The figures thereof indi- cate ... See full document
8
Indicative Tweet Generation: An Extractive Summarization Problem?
... an extractive summariza- tion ...source text and the target tweet play, and find that it is easier to extract tweets from more formal articles than less formal ... See full document
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