News2Images: Automatically summarizing news articles into image-based contents via deep learning

Jung Woo Ha, Dongyeop Kang, Hyuna Pyo, Jeonghee Kim

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


Compact representation is a key issue for effective information delivery to users in mobile content-providing services. In particular, it is more severe when providing text documents such as news articles on the mobile service. Here we propose a method for generating compact image-based contents from news documents (News2Image). The proposed method consists of three modules for summarizing news into a few key sentences based on the sematic similarity and diversity, converting the sentences into images, and generating contents consisting of sentence-embedded images. We use word embedding for document summarization and convolutional neural networks (CNNs) for sentence-to-image transformation. These image-based contents improve the readability, thus effectively delivering the core contents of the news to users. We demonstrate the news-to-image content generation on more-than one million Korean news articles using the proposed News2Image. Experimental results show our method generates better image-contents semantically related to the given news articles compared to a baseline method. Furthermore, we discuss some directions for applying News2Images to a news recommendation system.

Original languageEnglish (US)
Pages (from-to)27-32
Number of pages6
JournalCEUR Workshop Proceedings
StatePublished - 2015
Externally publishedYes
Event3rd International Workshop on News Recommendation and Analytics, INRA 2015 - Vienna, Austria
Duration: Sep 20 2015 → …

Bibliographical note

Publisher Copyright:
Copyright © 2015 for the individual papers by the papers' authors.


  • Automatic content generation
  • Deep learning
  • Image-based contents
  • Mobile service
  • News-to-images
  • Summarization


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