Applying Software Model Checking to Automatic Text Summarization SSSEV2011 Irina Shoshmina, Nasrin Mostafazadeh, Omid Bakhshandeh, Alexey Belyaev, and Yuri Perov Sharif University Of Technology, Iran Saint Petersburg State Polytechnic University Siberian Federal University Under Supervision of: Dr. Natasha Sharygina In God We Trust

Natural Language Processing (NLP) 6/26/ :25 PM Definition : Computer analysis and generation of “natural language” text or speech. Computer ScienceLinguistics Artificial intelligence 2

NLP Dreams What if Computers could speak by your own language What if you yourself talk with your pc by voice and do your tasks What if to omit any programming language between us and computers 6/26/ :25 PM3 What about programming languages verification techniques…?

The Borderline Between Natural Languages and Programming Languages Mathematical theory of languages proposed by Noam Chomsky in the 1950s. Chomsky and followers soon rejected this mathematical formalism as too weak for describing the rich structure of natural languages The theory of context free languages became part of the core of theoretical computer science. Widely used in artificial languages! 6/26/ :25 PM4

5 Is the problem mathematically model-able? No Algorithmic Rules ! No Mathematical Model !

Automatic Text Summarization (ATS) 6/26/ :25 PM Definition of Text Summarization : - The process to reduce the length or complexity of the original text, without losing the main content, keeping important info and also readability. Query-Based ATS: Question answering systems are IR systems that differ from traditional search engines in two ways: – users can express their information need as natural language questions – the result of the system is an answer to a question (summarized from some relevant texts) Temporal question answering system: – Deducing which qualitative temporal relation holds between two events; a complicated reasoning task! 6

The Main Idea Using model checking approach to solve the problem of query-based text summarization, in order to get the most informative and readable output. 6/26/ :25 PM7

Big Picture! temporal annotated text finite formal model query natural language t ext temporal tagged query linear temporal formula counter example tagged counter example Readable and informative Summary NLP processing translating model checking translating NLP processing

Any Questions? 6/26/ :25 PM9 Thanks for your attention.... Thanks SSSEV2011 for encouraging us to ignite this joint research…