Automatic Amharic News Text Summarizer (extraction)

It is visible that the amount of textual information output is highlyrnincreasing from day to day. Compared to the text output the human capacity ofrnreading is almost negligible. This big difference creates a problem inrncommunicating information to the best possible extent. Managing the output alsornbecomes very difficult. Tasks of sorting, searching through and categorizing arernturning out to be cumbersome. The limited carrying capacities of therncommunication channels also require huge reduction in size.rnThe focus of this research is on development of a mechanism forrnshortening Amharic news texts and for producing concise summaries of them. Thernsystem !lies to pin point the most important sentences of the original text andrnextract them as a summary of the news. Thus the extract is a lot shorter andrnpainless to handle.rnThe proposed summarizer uti I izes several statistical techniques, locationrnheuristics and diagnostic units to determine the parts of the text to be extracted.rnSelected information retrieval and text mining techniques are adopted to build arnmodel for the proposed system.rnThe application of the system alter adjusting the weight of its diagnosticrnunits by using four Amharic news items in 124 different ways reveals a promisingrnresult in automating the task of generating news summaries. Human generatedrnsummaries are used for adjusting weight and evaluating the system. Finally 58%rnRecall and 70.4% Precision values are attained. Based on this result, further workrnis recommended for future improvements of this system and studies in the area ofrnautomatic Amharic text summarize

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