Generating Acrostics via Paraphrasing and Heuristic Search

We consider the problem of automatically paraphrasing a text in order to find an equivalent text that contains a given acrostic. A text contains an acrostic, if the first letters of a range of consecutive lines form a word or phrase. Our approach turns this paraphrasing task into an optimization problem: we use various existing and also new paraphrasing techniques as operators applicable to intermediate versions of a text (e.g., replacing synonyms), and we search for an operator sequence with minimum text quality loss. The experiments show that many acrostics based on common English words can be generated in less than a minute. However, we see our main contribution in the presented technology paradigm: a novel and promising combination of methods from Natural Language Processing and Artificial Intelligence. The approach naturally generalizes to related paraphrasing tasks such as shortening or simplifying a given text.