text: While large-scale knowledge graphs provide vast amounts of structured facts about entities, a short textual description can often be useful to succinctly characterize an entity and its type. Unfortunately, many knowledge graph entities lack such textual descriptions. In this paper, we introduce a dynamic memory-based network that generates a short open vocabulary description of an entity by jointly leveraging induced fact embeddings as well as the dynamic context of the generated sequence of words. We demonstrate the ability of our architecture to discern relevant information for more accurate generation of type description by pitting the system against several strong baselines.
  @cite_9: The staggering amounts of content readily available to us via digital channels can often appear overwhelming. While much research has focused on aiding people at selecting relevant articles to read, only few approaches have been developed to assist readers in more efficiently reading an individual text. In this paper, we present HiText, a simple yet effective way of dynamically marking parts of a document in accordance with their salience. Rather than skimming a text by focusing on randomly chosen sentences, students and other readers can direct their attention to sentences determined to be important by our system. For this, we rely on a deep learning-based sentence ranking method. Our experiments show that this results in marked increases in user satisfaction and reading efficiency, as assessed using TOEFL-style reading comprehension tests.
summary: Generating entity descriptions is related to the task of text summarization. Most traditional work in this area was extractive in nature, i.e. it selects the most salient sentences from a given input text and concatenates them to form a shorter summary or presents them differently to the user @cite_9 . Abstractive summarization goes beyond this in generating new text not necessarily encountered in the input, as is typically necessary in our setting. The surge of sequence-to-sequence modeling of text via LSTMs naturally extends to the task of abstractive summarization by training a model to accept a longer sequence as input and learning to generate a shorter compressed sequence as a summary.