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2017-4-10-7056.dvi
Ayana, Shen SQ, Lin YK et al. Recent advances on neural headline generation. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 32(4): 768–784 July 2017. DOI 10.1007/s11390-017-1758-3 Recent Advances on Neural Headline Generation Ayana 1,2,3,4 , Student Member, CCF, Shi-Qi Shen 1,2,3 , Student Member, CCF Yan-Kai Lin 1,2,3,5 , Student Member, CCF, Cun-Chao Tu 1,2,3,5 , Student Member, CCF, Yu Zhao 1,2,3 Z
A Survey on Neural Network-Based Summarization Methods
Automatic text summarization, the automated process of shortening a text while reserving the main ideas of the document(s), is a critical research area in natural language processing. The aim of this literature review is to survey the recent work on neural-based models in automatic text summarization. We examine in detail ten state-of-the-art neural-based summarizers: five abstractive models and f
GitHub - mathsyouth/awesome-text-summarization: A curated list of resources dedicated to text summarization
sumeval implemented in Python is a well tested & multi-language evaluation framework for text summarization. sumy is a simple library and command line utility for extracting summary from HTML pages or plain texts. The package also contains simple evaluation framework for text summaries. Implemented summarization methods are Luhn, Edmundson, LSA, LexRank, TextRank, SumBasic and KL-Sum. TextRank4ZH
Text Summarization in Python: Extractive vs. Abstractive techniques revisited | RARE Technologies
This blog is a gentle introduction to text summarization and can serve as a practical summary of the current landscape. It describes how we, a team of three students in the RaRe Incubator programme, have experimented with existing algorithms and Python tools in this domain. We compare modern extractive methods like LexRank, LSA, Luhn and Gensim’s existing TextRank summarization module on the Opino
seq2seq icml tutorial
Oriol Vinyals @OriolVinyalsML & Navdeep Jaitly @NavdeepLearning Sequence-To-Sequence (Seq2Seq) learning was introduced in 2014, and has since been extensively studied and extended to a large variety of domains. Seq2Seq yields state-of-the-art performance on several applications such as machine translation, image captioning, speech generation, or summarization. In this tutorial, we will survey the
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505 � � � � Original Paper Text Summarization Model based on Maximum Coverage Problem and its Variant Hiroya Takamura Tokyo Institute of Technology, Precision and Intelligence Laboratory takamura@pi.titech.ac.jp, http://www.lr.pi.titech.ac.jp/~takamura/ Manabu Okumura oku@pi.titech.ac.jp, http://www.lr.pi.titech.ac.jp/~oku/ keywords: text summarization, decoding algorithm, approximate algorithm, i
Encoder-Decoder Models for Text Summarization in Keras - MachineLearningMastery.com
Text summarization is a problem in natural language processing of creating a short, accurate, and fluent summary of a source document. The Encoder-Decoder recurrent neural network architecture developed for machine translation has proven effective when applied to the problem of text summarization. It can be difficult to apply this architecture in the Keras deep learning library, given some of the
Controllable Abstractive Summarization
Current models for document summarization disregard user preferences such as the desired length, style, the entities that the user might be interested in, or how much of the document the user has already read. We present a neural summarization model with a simple but effective mechanism to enable users to specify these high level attributes in order to control the shape of the final summaries to b
A Deep Reinforced Model for Abstractive Summarization
Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. For longer documents and summaries however these models often include repetitive and incoherent phrases. We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new tr
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GitHub - pltrdy/OpenNMT-py at drmas
About "A Deep Reinforced Model for Abstractive Summarization"
https://www.anlp.jp/proceedings/annual_meeting/2016/pdf_dir/D1-3.pdf
Text Summarization Evaluation - BLEU vs ROUGE
TAC 2008 Update Summarization Task
http://www.lrec-conf.org/proceedings/lrec2014/pdf/1093_Paper.pdf