In news aggregation systems focused on broad news domains, certain stories may appear in multiple articles. Depending on the relative importance of the story, the number of versions can reach dozens or hundreds within a day. The text in these versions may be nearly identical or quite different. Linking multiple versions of a story into a single group brings several important benefits to the end-user–reducing the cognitive load on the reader, as well as signaling the relative importance of the story. We present a grouping algorithm, and explore several vector-based representations of input documents: from a baseline using keywords, to a method using salience–a measure of importance of named entities in the text. We demonstrate that features beyond keywords yield substantial improvements, verified on a manually-annotated corpus of business news stories.

Task 5 of SemEval-2017 involves fine-grained sentiment analysis on financial microblogs and news. Our solution for determining the sentiment score extends an earlier convolutional neural network for sentiment analysis in several ways. We explicitly encode a focus on a particular company, we apply a data augmentation scheme, and use a larger data collection to complement the small training data provided by the task organizers. The best results were achieved by training a model on an external dataset and then tuning it using the provided training dataset.