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 language||English (US)|
|Number of pages||6|
|Journal||CEUR Workshop Proceedings|
|State||Published - 2015|
|Event||3rd International Workshop on News Recommendation and Analytics, INRA 2015 - Vienna, Austria|
Duration: Sep 20 2015 → …
Bibliographical notePublisher Copyright:
Copyright © 2015 for the individual papers by the papers' authors.
- Automatic content generation
- Deep learning
- Image-based contents
- Mobile service