The demand for a tool for summarizing emerging topics is increasing in modern life since the tool can deliver well-organized information to its users. Even though there are already a number of successful search systems, the system which automatically summarizes and organizes the content of emerging topics is still in its infancy. To fulfill such demand, we introduce an automated report generation system that generates a well-summarized human-readable report for emerging topics. In this report generation system, emerging topics are automatically discovered by a topic model and news articles are indexed by the discovered topics. Then, a topical summary and a timeline summary for each topic is generated by a topical multi-document summarizer and a timeline summarizer respectively. In order to enhance the apprehensibility of the users, the proposed report system provides two report modes. One is Today's Briefing which summarizes five discovered topics of every day, and the other is Full Report which shows a long-term view of each topic with a detailed topical summary and an important event timeline.

Dialog state tracking in spoken dialog system is the task that tracks the flow of a dialog and grasps what a user wants from the utterance precisely. Since the dialog success is related to catching the want of the user, dialog state tracking is a necessary component for spoken dialog systems. This paper proposes a neural dialog state tracker with the attention mechanism for focusing on valuable words and the hierarchical softmax for efficient training of the tracker. In addition, the proposed tracker combines a natural language understanding module and a dialog state module in an end-to-end style. As a result, the error propagation within a dialog system is minimized. To prove the effectiveness of the proposed model, we do experiments on dialog state tracking in the human-human task-oriented dialogs. Our experimental results show that the proposed method outperforms both the neural tracker without the attention mechanism and that without the hierarchical softmax.

The number of natural language queries submitted to search engines is increasing as search environments get diversified. However, legacy search engines are still optimized for short keyword queries. Thus, the use of natural language queries at legacy search engines degrades the retrieval performance of the engines. This paper proposes a novel method to translate a natural language query into a keyword query relevant to the natural language query for retrieving better search results without change of the engines. The proposed method formulates the translation as a generation task. That is, the method generates a keyword query from a natural language query by preserving the semantics of the natural language query. A recurrent neural network encoder-decoder architecture is adopted as a generator of keyword queries from natural language queries. In addition, an attention mechanism is also used to cope with long natural language queries.

Readability is used to provide users with highquality service in text recommendation or text visualization.With the increasing use of handheld devices, reading device is regarded as an important factor for readability.Therefore, this paper investigates the relationship between readability and reading devices such as a smart phone, a tablet, and paper.We suggest readability factors that are strongly related with the readability of a specific device by showing the correlations between various factors in each device and human-rated readability.Our experimental results show that each device has its own readability characteristics, and thus different weights should be imposed on readability factors according to the device type.In order to prove the usefulness of the results, we apply the device-dependent readability to news article recommendation.

The interest of users in handheld devices is strongly related to their location. Therefore, the user location is important, as a user context, for news article recommendation in a mobile environment. This paper proposes a novel news article recommendation that reflects the geographical context of the user. For this purpose, we propose the Explicit Localized Semantic Analysis (ELSA), an ESA-based topical representation of documents. Every location has its own geographical topics, which can be captured from the geo-tagged documents related to the location. Thus, not only news articles but locations are also represented as topic vectors. The main advantage of ELSA is that it stresses only the topics that are relevant to a given location, whereas all topics are equally important in ESA. As a result, geographical topics have different importance according to the user location in ELSA, even if they come from the same article. Another advantage of ELSA is that it allows a simple comparison of the user location and news articles, because it projects both locations and articles onto an identical space composed of Wikipedia topics. In the evaluation of ELSA with the New York Times corpus, it outperformed two simple baselines of Bag-Of-Words and LDA as well as two ESA-based methods. Rt10 of ELSA was improved up to 46.25% over other methods, and its [email protected] was always higher than those of the others regardless of k.