Using genre-specific features for patent summaries.

Targeted summarization technique for patent material.Segment as intra-sentence summarization unit.Exploitation of lexical chains across the whole patent document.Full-fledged text generation techniques for summarization. Patent search is recall-driven, which goes hand in hand with at least a partial sacrifice of precision. As a consequence, patent analysts have to regularly view and examine a large amount of patents. This implies a very high workload. Interactive analysis aids that help to minimize this workload are thus of high demand. Still, these aids do not reduce the amount of the material to be examined, they only facilitate its examination. Its reduction can be achieved working with patent summaries instead of full patent documents. So far, high quality patent summaries are produced mainly manually and only a few research works address the problem of automatic patent summarization. Most often, these works either replicate the summarization metrics known from general discourse summarization or focus on the claims of a patent. However, it can be observed that neither of the strategies is adequate: general discourse state-of-the-art summarization techniques are of limited use due to the idiosyncrasies of the patent genre, and techniques that focus on claims only miss in their summaries important details provided in the other sections on the components of the invention introduced in the claims. We propose a patent summarization technique that takes the idiosyncrasies of the patent genre (such as the unbalanced distribution of the content across the different sections of a patent, excessive length of the sentences in the claims, abstract vocabulary, etc.) into account to obtain a comprehensive summary of the invention. In particular, we make use of lexical chains in the claims and in the description of the invention and of aligned claimdescription segments at the subsentential level to assess the relevance of the individual fragments of the document for the summary. The most relevant fragments are selected and merged using full-fledged natural language generation techniques.