Proceedings ArticleDOI
Extractive document summarization based on convolutional neural networks
Yong Zhang,Meng Joo Er,Mahardhika Pratama +2 more
- pp 918-922
Reads0
Chats0
TLDR
A document summarization framework based on convolutional neural networks is successfully developed to learn sentence features and perform sentence ranking jointly and adapt the original CNN model to address a regression process for sentence ranking.Abstract:
Extractive summarization aims to generate a summary by ranking sentences, whose performance relies heavily on the quality of sentence features. In this paper, a document summarization framework based on convolutional neural networks is successfully developed to learn sentence features and perform sentence ranking jointly. We adapt the original CNN model to address a regression process for sentence ranking. Pre-trained word vectors are used to enhance the performance of our model. We evaluate our proposed method on the DUC 2002 and 2004 datasets covering single and multi-document summarization tasks respectively. The proposed system achieves competitive or even better performance compared with state-of-the-art document summarization systems.read more
Citations
More filters
Journal ArticleDOI
Convolution Neural Network for Text Mining and Natural Language Processing
TL;DR: In this article, the authors surveyed more than 30 scientific articles obtained from scientific article portals such as Science Direct, IEEE explore, Arxiv, and Google Scholar and obtained similarities and differences based on the problem solved, the pre-processing method for data input, and the approach taken to achieve the goal.
Journal ArticleDOI
A topic modeled unsupervised approach to single document extractive text summarization
TL;DR: In this article , Latent Dirichlet allocation was used for topic modeling, while K-Medoids clustering was employed for summary generation, and the proposed framework offered scores of 34.80%, 9.13%, and 32.30% on Wikihow, CNN/DailyMail, and DUC2002 corpus.
Journal ArticleDOI
An attention enhanced sentence feature network for subtitle extraction and summarization
Chalothon Chootong,Chalothon Chootong,Timothy K. Shih,Ankhtuya Ochirbat,Worapot Sommool,Yung Yu Zhuang +5 more
TL;DR: In this article, a novel multiple attention mechanism for subtitle summarization is introduced to address the problem of content overloading and improve the performance of video retrieval. But, the model is not suitable for the task of video classification.
Journal ArticleDOI
Deep Learning – Now and Next in Text Mining and Natural Language Processing
TL;DR: In this paper, a literature review has been conducted to find out what has not been discussed in last research in domain text mining and NLP using deep learning, which is due to the assumption that input data is very important in performance of an algorithm.
Proceedings ArticleDOI
Extractive Document Summarization Using a Supervised Learning Approach
TL;DR: The proposed model uses a convolutional neural networks which is capable of learning sentence features on its own for sentence ranking and its performance is found to be competitive or better in comparison with state-of-the-art systems.
References
More filters
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
Proceedings ArticleDOI
Convolutional Neural Networks for Sentence Classification
TL;DR: The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification, and are proposed to allow for the use of both task-specific and static vectors.
Proceedings Article
ROUGE: A Package for Automatic Evaluation of Summaries
TL;DR: Four different RouGE measures are introduced: ROUGE-N, ROUge-L, R OUGE-W, and ROUAGE-S included in the Rouge summarization evaluation package and their evaluations.
Posted Content
Convolutional Neural Networks for Sentence Classification
TL;DR: In this article, CNNs are trained on top of pre-trained word vectors for sentence-level classification tasks and a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks.