IntoNews: Online news retrieval using closed captions

• IntoNews is an Intonow app for matching web news with news broadcasted on TV.
• We present a framework that models the task as 2 separate sub-tasks: Find & retrieve.
• We design and publicly provide an evaluation testbed for the IntoNews problem.
• Quality evaluation has to trade-off 2 competing metrics: coverage and precision.

We present IntoNews, a system to match online news articles with spoken news from a television newscasts represented by closed captions. We formalize the news matching problem as two independent tasks: closed captions segmentation and news retrieval. The system segments closed captions by using a windowing scheme: sliding or tumbling window. Next, it uses each segment to build a query by extracting representative terms. The query is used to retrieve previously indexed news articles from a search engine. To detect when a new article should be surfaced, the system compares the set of retrieved articles with the previously retrieved one. The intuition is that if the difference between these sets is large enough, it is likely that the topic of the newscast currently on air has changed and a new article should be displayed to the user. In order to evaluate IntoNews, we build a test collection using data coming from a second screen application and a major online news aggregator. The dataset is manually segmented and annotated by expert assessors, and used as our ground truth. It is freely available for download through the Webscope program.1 Our evaluation is based on a set of novel time-relevance metrics that take into account three different aspects of the problem at hand: precision, timeliness and coverage. We compare our algorithms against the best method previously proposed in literature for this problem. Experiments show the trade-offs involved among precision, timeliness and coverage of the airing news. Our best method is four times more accurate than the baseline.