Early work in the computational treatment of natural language focused on summarization, and machine translation. In my research I have concentrated on the area of summarization of documents in different languages. This thesis presents my work on multi-lingual text similarity. This work enables the identification of short units of text (usually sentences) that contain similar information even though they are written in different languages. I present my work on SimFinderML, a framework for multi-lingual text similarity computation that makes it easy to experiment with parameters for similarity computation and add support for other languages. An in-depth examination and evaluation of the system is performed using Arabic and English data. I also apply the concept of multi-lingual text similarity to summarization in two different systems. The first improves readability of English summaries of Arabic text by replacing machine translated Arabic sentences with highly similar English sentences when possible. The second is a novel summarization system that supports comparative analysis of Arabic and English documents in two ways. First, given Arabic and English documents that describe the same event, SimFinderML clusters sentences to present information that is supported by both the Arabic and English documents. Second, the system provides an analysis of how the Arabic and English documents differ by presenting information that is supported exclusively by documents in only one language. This novel form of summarization is a first step at analyzing the difference in perspectives from news reported in different languages.
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