ACM/IMS transactions on data science最新文献_第5页

Effective Discovery of Meaningful Outlier Relationships 有效发现有意义的离群值关系

ACM/IMS transactions on data science Pub Date : 2019-10-19 DOI: 10.1145/3385192

Aline Bessa, J. Freire, T. Dasu, D. Srivastava

引用次数: 4

Multifaceted Analysis of Fine-Tuning in a Deep Model for Visual Recognition 视觉识别深度模型中微调的多方面分析

ACM/IMS transactions on data science Pub Date : 2019-07-11 DOI: 10.1145/3319500

Xiangyang Li, Luis Herranz, Shuqiang Jiang

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引用次数: 1

Neural Abstractive Text Summarization with Sequence-to-Sequence Models 基于序列到序列模型的神经抽象文本摘要

ACM/IMS transactions on data science Pub Date : 2018-12-05 DOI: 10.1145/3419106

Tian Shi, Yaser Keneshloo, Naren Ramakrishnan, C. Reddy

{"title":"Neural Abstractive Text Summarization with Sequence-to-Sequence Models","authors":"Tian Shi, Yaser Keneshloo, Naren Ramakrishnan, C. Reddy","doi":"10.1145/3419106","DOIUrl":"https://doi.org/10.1145/3419106","url":null,"abstract":"In the past few years, neural abstractive text summarization with sequence-to-sequence (seq2seq) models have gained a lot of popularity. Many interesting techniques have been proposed to improve seq2seq models, making them capable of handling different challenges, such as saliency, fluency and human readability, and generate high-quality summaries. Generally speaking, most of these techniques differ in one of these three categories: network structure, parameter inference, and decoding/generation. There are also other concerns, such as efficiency and parallelism for training a model. In this article, we provide a comprehensive literature survey on different seq2seq models for abstractive text summarization from the viewpoint of network structures, training strategies, and summary generation algorithms. Several models were first proposed for language modeling and generation tasks, such as machine translation, and later applied to abstractive text summarization. Hence, we also provide a brief review of these models. As part of this survey, we also develop an open source library, namely, Neural Abstractive Text Summarizer (NATS) toolkit, for the abstractive text summarization. An extensive set of experiments have been conducted on the widely used CNN/Daily Mail dataset to examine the effectiveness of several different neural network components. Finally, we benchmark two models implemented in NATS on the two recently released datasets, namely, Newsroom and Bytecup.","PeriodicalId":93404,"journal":{"name":"ACM/IMS transactions on data science","volume":"1197 1","pages":"1 - 37"},"PeriodicalIF":0.0,"publicationDate":"2018-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85273720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 166

test paper to update gold OA language 试卷更新金OA语言

ACM/IMS transactions on data science Pub Date : 1900-01-01 DOI: 10.1145/3396300

引用次数: 0