[PDF] Top 20 Automatic Labeling of Topic Models Using Text Summaries
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Automatic Labeling of Topic Models Using Text Summaries
... the summaries to users unless they want to see ...the topic summaries, we can understand what the two topics are really ...first topic is about data analysis and data integra- tion, while the ... See full document
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Topic Models for Image Annotation and Text Illustration
... Recent years have witnessed the rapid growth of im- age collections available for searching and browsing over the Internet. Although image search engines are still in their infancy, initial research suggests that the ... See full document
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Generating Summaries with Topic Templates and Structured Convolutional Decoders
... neural text generation, it has been been recognized as useful for summariza- ...get summaries and use it to extract salient facts from the ...target topic and use a global optimi- sation algorithm to ... See full document
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Using Rhetorical Topics for Automatic Summarization
... complete text was summarized by one of these ...each topic was summarized, and the outputs were com- bined to create a summary of the whole ...original text, where length is measured in sen- tences. ... See full document
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Automatic Labelling of Topic Models Learned from Twitter by Summarisation
... Sum Basic (SB) This is a frequency based sum- marisation algorithm (Nenkova and Vanderwende, 2005), which computes initial word probabilities for words in a text. It then weights each sen- tence in the text ... See full document
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Towards Summarization for Social Media Results of the TL;DR Challenge
... true summaries provided by the authors of a post, they often abstract over a subject matter, and they cover a much wider range of topics than generally found in news ...truth summaries in the news and the ... See full document
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A Comparative Study of Mixture Models for Automatic Topic Segmentation of Multiparty Dialogues
... linear topic segmen- tation by exploiting word distributions in the input text, the focus of this article was on both comparing theoretical aspects and experimental results of two probabilistic mixture ... See full document
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Generating Topic Oriented Summaries Using Neural Attention
... quence models which generated text word-by- word (Sutskever et ...network models have been proposed for sum- marizing long ...2016) using a pointer network (Vinyals et ...port ... See full document
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Topic Models and Fusion Methods: a Union to Improve Text Clustering and Cluster Labeling
... In this paper, we propose a method to enrich document representation vectors to be used in partitional text clustering and cluster labeling. Our method is an unsupervised approach, needless of any external ... See full document
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Automatic Labeling of Problem Solving Dialogues for Computational Microgenetic Learning Analytics
... sentence labeling, in which every sentence is an ...the models is that they do not make use of con- text, but focus on representing each sentence independently (Dernoncourt et ... See full document
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Malayalam Text Summarization Using Graph Based Method
... Text summarization methods can be classified into extractive and abstractive summarization (Hovy and Lin, 1997) [4]. Abstractive text summarization, it understands the original text and re-tells it ... See full document
5
Supervised Machine Learning for Extractive Query Based Summarisation of Biomedical Data
... full text into a compact version while preserving the crucial information of the original text that is rel- evant to a ...digital text over the internet has reached such tremendous magnitude that a ... See full document
9
Building Systematic Reviews Using Automatic Text Classification Techniques
... The amount of information in medical publications continues to increase at a tremendous rate. Systematic reviews help to process this growing body of informa- tion. They are fundamental tools for evi- dence-based ... See full document
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Decision Tree Models Applied to the Labeling of Text with Parts of Speech
... Decision Tree Models Applied to the Labeling of Text with Parts of Speech D e c i s i o n Tree M o d e l s A p p l i e d to t h e L a b e l i n g o f T e x t w i t h P a r t s o f S p e e c h Ezra Bla[.] ... See full document
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Automatic Evaluation of Summaries Using N gram Co occurrence Statistics
... of automatic evaluations should be a good predictor of the statistical signifi- cance of human assessments with high ...an automatic evaluation will do the same with high ...an automatic evaluation ... See full document
8
Social group recommendation using topic models
... the topic oriented user analysis to obtain the user influence on specific ...specific topic and user modules, and the experimental results show that our method outperforms than the ... See full document
6
Studying the History of Ideas Using Topic Models
... How can we identify and study the exploration of ideas in a scientific field over time, noting periods of gradual development, major ruptures, and the wax- ing and waning of both topic areas and connections with ... See full document
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Framework of Automatic Text Summarization Using Reinforcement Learning
... of automatic text summarization called Au- tomatic Summarization using Reinforcement Learning (ASRL) in this paper, which models the process of constructing a summary within the framework of ... See full document
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A Multimodal LDA Model integrating Textual, Cognitive and Visual Modalities
... the text-only model. Not only do all 6 hy- brid models do significantly better than the text-only models, they show a highly significant improvement over their individual components (p < ... See full document
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Rough Set Based Opinion Mining in Tamil
... Indiscernibility relation reduces the data by identifying equivalence feature, i.e. entity that is indiscernible, using the available attributes. Only one element of the equivalence feature is needed to represent ... See full document
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