Abstract
Conventional methods for text summarization are mostly based on the idea of selecting important sentences from a set of given sentences such as a paragraph or a whole text. Those methods have a merit that each selected sentence remains unchanged and is thus correct. However, it is sometimes necessary to shorten each sentence, when a higher compaction rate is required, or when a paragraph-by-paragraph summarization is not adequate. In such sentence compaction, it is important that a shortened sentence is natural as a Japanese sentence. In this paper, the sentence compaction problem is formulated as “a problem of selecting a subsequence of phrases from a given sentence that maximizes the sum of phrase significance scores and inter-phrase dependency scores.” Then, an efficient algorithm to solve this problem is proposed. Since this method takes inter-phrase dependency into account, a shortened sentence is expected to be grammatically correct and natural. This paper is focused on the derivation, computational complexity, and implementation issues of the algorithm, and will not discuss the matter of how to define the phrase significance score and the inter-phrase dependency score, though it will be a crucially important matter in practical applications.
