Titlebook: Database Systems for Advanced Applications; 26th International C Christian S. Jensen,Ee-Peng Lim,Chih-Ya Shen Conference proceedings 2021 T

深淵

Multi-label Classification of Long Text Based on Key-Sentences Extractioned global feature information. Some approaches that split an entire text into multiple segments for feature extracting, which generates noise features of irrelevant segments. To address these issues, we introduce key-sentences extraction task with semi-supervised learning to quickly distinguish rele

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Automated Context-Aware Phrase Mining from Text Corporatext into structured information. Existing statistic-based methods have achieved the state-of-the-art performance of this task. However, such methods often heavily rely on statistical signals to extract quality phrases, ignoring the effect of ...In this paper, we propose a novel context-aware method

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Keyword-Aware Encoder for Abstractive Text Summarizationn summarizing a text. Fewer efforts are needed to write a high-quality summary if keywords in the original text are provided. Inspired by this observation, we propose a keyword-aware encoder (KAE) for abstractive text summarization, which extracts and exploits keywords explicitly. It enriches word r

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Neural Adversarial Review Summarization with Hierarchical Personalized Attention and ignore different informativeness of different sentences in a review towards summary generation. In addition, the personalized information along with reviews (e.g., user/product and ratings) is also highly related to the quality of generated summaries. Hence, we propose a review summarization me

妨礙

Generating Contextually Coherent Responses by Learning Structured Vectorized Semanticso appropriately encode contexts and how to make good use of them during the generation. Past works either directly use (hierarchical) RNN to encode contexts or use attention-based variants to further weight different words and utterances. They tend to learn dispersed focuses over all contextual info

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Discriminant Mutual Information for Text Feature Selection because of high correlation between features; so, it is necessary to execute feature selection. In this paper, we propose a Discriminant Mutual Information (DMI) criterion to select features for text classification tasks. DMI measures the discriminant ability of features from two aspects. One is th

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