Classification of research papers using citation links and citation types: Towards automatic review article generation.

We are investigating automatic generation of a review (or survey) article in a specific subject domain. In a research paper, there are passages where the author describes the essence of a cited paper and the differences between the current paper and the cited paper (we call them citing areas). These passages can be considered as a kind of summary of the cited paper from the current author's viewpoint. We can know the state of the art in a specific subject domain from the collection of citing areas. FUrther, if these citing areas are properly classified and organized, they can act 8.', a kind of a review article. In our previous research, we proposed the automatic extraction of citing areas. Then, with the information in the citing areas, we automatically identified the types of citation relationships that indicate the reasons for citation (we call them citation types). Citation types offer a useful clue for organizing citing areas. In addition, to support writing a review article, it is necessary to take account of the contents of the papers together with the citation links and citation types. In this paper, we propose several methods for classifying papers automatically. We found that our proposed methods BCCT-C, the bibliographic coupling considering only type C citations, which pointed out the problems or gaps in related works, are more effective than others. We also implemented a prototype system to support writing a review article, which is based on our proposed method

Structure Extraction from Presentation Slide Information

Abstract. Electronic presentations are used in numerous scenarios, such as lectures and meetings. In recent years, the widespread use of electronic presentations means that presentation slide data is increasing as one of industry’s most important information resources. Therefore, it is neces-sary to develop a practical usage method for the reutilisation of the data on slides. An approach to achieve this is to focus on visual structure information within a slide, because visual structure information is one of the most valuable, easy to understand methods for humans. However, since visual structure information is not explicitly defined in the slide data itself, computers have difficulty comprehending structure informa-tion directly. In this paper, we propose a method of extracting structure information from slide information. The proposed method is composed of two steps: organising objects within the slide as units, such as title, body text, figure and table, and structuring the units as a hierarchy tree based on a top-down approach

Producing More Readable Extracts by Revising Them.

In this paper, we first experimentally investigated the factors that make extracts hard to read. We did this by having human subjects try to revise extracts to produce more readable ones. We then classified the factors into five, most of which are related to cohesion, after which we devised revision rnlcs for each factor, and partially implemented a system that revises extracts

Automatic Acquisition of Script Knowledge from a Text Collection

in this paper, we describe a method for automatic acquisition of script knowledge from a Japanese text collection. Script knowledge represents a typical sequence of actions that occur in a particular situation. We extracted sequences (pairs) of actions occurring in time order from a Japanese text collection and then chose those that were typical of certain situations by ranking these sequences (pairs) in terms of the frequency of their occurrence. To extract sequences of actions occurring in time order, we constructed a text collection in which texts describing facts relating to a similar situation were clustered together and arranged in time order