Linguistic Summaries On Small Screens
Author(s)
E. D’Avanzo & T. Kuflik
Abstract
Nowadays computers are becoming ever smaller and cheaper, and as a result
they have become available everywhere. One major drawback of the small
computing devices is their small screen (e.g., PDA's and smartphones) that
makes it harder to view large amounts of information. The problem is even
worse given the flood of information today's users are facing in their daily tasks.
This paper presents LAKE, a flexible summarization system consisting of
different independent linguistic modules that can easily be configured and
parameterized. The system can be used to reduce the amount of information
viewed by users of small-screen devices while maintaining its essence. The
system works as follows: a set of linguistically motivated candidate phrases is
identified. A learning device then chooses the best phrases. Finally, phrases at
the top of the ranking are merged to form a summary. LAKE is tested within the
framework of eSMEs, a joint Project of Italdata S.p.A. Siemens Business
Services Group and Multiplanning s.r.l. companies.
Keywords: text mining, machine learning, linguistic knowledge, keyphrase
extraction, summarization, small devices.
1 Introduction
Nowadays users are facing problems while viewing information displayed on
small screen devices (e.g. PDAs and Smartphones). Such devices require
presentation of the most essential information only, in order to allow their users
to use them effectively. Text summarization is a possible solution to such a
problem. Hahn and Mani [1] defined summarization as ‘the art of abstracting key
content from one or more information sources’. In fact, the importance of
Keywords
text mining, machine learning, linguistic knowledge, keyphrase
extraction, summarization, small devices.
Related Book
Other papers in this volume
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