Measuring similarity between texts is an important task for several applications.Available approaches to measure document similarity are inadequate for document pairs that have non-comparable lengths, such as a long document and its summary.This is because of the lexical, contextual and the abstraction gaps between a long document of rich details and its concise summary of abstract information.In this paper, we present a document matching approach to bridge this gap, by comparing the texts in a common space of hidden topics.We evaluate the matching algorithm on two matching tasks and find that it consistently and widely outperforms strong baselines.We also highlight the benefits of the incorporation of domain knowledge to text matching.

In this paper we present a novel framework for morpheme segmentation which uses the morpho-syntactic regularities preserved by word representations, in addition to orthographic features, to segment words into morphemes.This framework is the first to consider vocabulary-wide syntactico-semantic information for this task.We also analyze the deficiencies of available benchmarking datasets and introduce our own dataset that was created on the basis of compositionality.We validate our algorithm across different datasets and languages and present new state-of-the-art results.