Generation of Informative Texts with Style

An approach to the computational treatment of style is presented in the case of generation of informative texts. We regard the style mestly as a me,as of controlled selection of alternatives faced at each level of text generation. The generation technique, as well as the style specification, are considered at four levels-content production, discourse generation, surface structure development, and lexicai choice. A style is specified by the frequency of occurrence of certain features examined through observation of particular texts. The algorithm for text generation ensures efficient treatment of the style requirements. The manner of presentation, i.e. the repeating pattern of expression produced by a given subject or ill a certain community, is known as style. Thus each newspaper renders the weather information in n specific style by adopting n particular scheme of presentation. We assume that a text generator, like humans, should has its own style. Furthermore, we regard the style as a means to control the selection of particular constructs among the great variety provided by the language. In this paper , we study what defines a style and how a system could produce texts with style. The problem is tackled in the framework of text generation created by some of the pioneering works in the field [9,10]. According to this framework, the process of generation is considered at four levels: content production, discourse planning, surface structure generation and lexical choice. We trace out the features that characterize the texts generated from a given content portion in the case of informative texts (introduced in section 2) and show how they can be treated eomputationally. For illustration of our considerations we make use of the class of weather reports-informative texts about which a good deal of material has been collected, mostly through MF,-TEOVIS-an experimental system for handling multi-modal weather reports. The development of the METEOVIS project began with the transformation of weather forecasts from text to map [5]. Then we studied the conversion of weather forecast texts into texts with another discourse structure or in another language [6]. This year, the system was redesigned so that multimodal weather products could be generated from dntasets [7]. The domain specific knowledge was isolated in knowledge bases (terminological, rhetorical and grammatical), and the processing modules were made independent from the subject domain as much as possible. At this point we became aware that additional information was necessary to produce high quality texts. Thus …