CAST: A computer-aided summarisation tool

CAST: a Computer-Aided Summarisation Tool Constantin Or5san, Ruslan Mitkov and Laura Hasler Research Group in Computational Linguistics University of Wolverhampton {C.Orasan, R.Mitkov, L.Hasler}@wlv.ac.uk Abstract In this paper we propose computer- aided summarisation (CAS) as an alternative approach to automatic summarisation, and present an ongoing project which aims to develop a CAS system. The need for such an alternative approach is justified by the relatively poor performance of fully automatic methods used in summarisation. Our system combines several summarisation methods, allowing the user of the system to interact with their parameters and output in order to improve the quality of the produced summary. 1 Introduction The information overload predicted a few years ago has become a reality. Recent reports showed that approximately 24 terabytes of text are recorded each year (Lyman and Varian, 2000). 1 Given these values, it becomes increasingly difficult to keep up with the news or find specific information which was produced in the past. Computers play an important role in sifting through information by performing tasks such as classification, retrieval and summarisation. Automatic summarisation systems help us to deal with the information overload by reducing it. At present the most common type of summarised information is textual information, but unfortunately the quality of the automatic summaries is not of a very high level. 'This figure includes books, newspapers, scholarly journals, office documents, etc. In light of this problem, we propose computer- aided summarisation (CAS) as an alternative to automatic summarisation (AS). Whereas AS does not require any human input to produce summaries, we argue that CAS is a more feasible approach as it allows the user to post- edit the automatic summaries according to their requirements. In this paper we present an ongoing project which in the process of developing CAS environment. The structure of the paper is as follows: In Section 2 we outline related work. Section 3 discusses the objectives of our research, followed by the features of a CAS prototype in the next section. A discussion of current findings and future plans are presented in Section 5, and the paper finishes with concluding remarks. 2 Related work Apart from a working paper of ours in the mid 90s (Mitkov, 1995), the only relevant research we could find in this field is that of Craven (Craven, 1996). However, Craven's approach takes a rather simplistic view because it uses only methods which extract keywords from the text and not complete sentences or even phrases. Another tool which aids humans in producing summaries is presented in (Narita, 2000). This tool does not employ any automatic methods to help humans, but gives them the option to access a corpus of human produced abstracts which can function as templates, providing grammatical patterns and collocations common to abstracts. Endres-Niggemeyer (Endres-Niggemeyer, 1998) identifies three stages in human summarisation: document exploration, relevance assessment and summary production. In the first 135 two stages the summariser identifies the overall structure of the text and the main topics, whereas in the third one, copy and …

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