To test our Xhosa text summarizer, a collection of 200 news items documents from the Xhosa online newspaper, IsiGidimi.co.za, an online Xhosa newspaper was accomplished. The documents are downloaded and saved in the text file format. The authors consider only one reference summary for evaluation for each document in the corpus. Evaluation of a system-generated summary is done by comparing it to the reference summary. There is fixed percentage of summary for auto summarization, which is 50%, which will reduce the summary into half of its original form.
FIGURE 1 shows the interface of Xhosa text summarizer and is the followed TABLE 4, which shows the results of English text summarizer. TABLE 5 shows the results from our text summarizer IsiXhoSum. FIGURE 2 show the relevancy of our system to manually summarizer text, the summaries made by English text summarizer.
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RESULTS OF THE ENGLISH SYSTEM
Text ID Original Length Summary Length summary ratio
Text 1 1422
638 55.1
Text 2 1472
525
64.3
Text 3 2954
853 71.1
Text 4 1547 866 44.0
Text 5 1555
1026
34.0
Text 5 1874 814 56.6
Text 6 2044 865 57.7
Text 7 2282 829 63.7
Text 8 1656 864 47.8
Text9 2285
558 75.6
Text 10 1865
899 51.8
Text 11 2034 595 70.7
Text 12 2171 807 62.8
Text 13 2584
938 63.7
Text 14 1422
638 55.1
Text 15 1472
525
64.3
Text ID Original Length Summary Length summary ratio
Text 1 2572 855 66.7
Text 2 2160 909 57.9
Text 3 2574 855 66.7
Text 4 2166 226 89.5
Text 5 4359 1587 63.5
Text 5 2279 598 73
Text 6 3046 661 78.2
Text 7 1650 329 80.0
Text 8 2280 932 59.1
Text9 2040 706 65.3
Text 10 1862 836 55.1
Text 11 1864 232 87.5
Text 12 1549 173 88.8
Text 13 4070 424 89.5
Text 14 2915 191 93.4
Text 15 2572 855 66.7
Text 14 1422
638 55.1
Figure. 2 RELEVANCY OF OUR SYSTEM WITH MANUALLY GENRATED SUMMARIES
VIII.CONCLUSION AND FURURE WORK
This study makes use the extraction method for isiXhosa text summarization. Sentences have been extracted the according to their weight and this is done by maintaining their order. The first sentence is kept with the notion that every first sentence has sort of a significance and therefore should be given first priority.
The summarization method used is extraction based; when important sentences are extracted, it is possible that there might be a proper noun on sentence and the sentence on the other one has a problem, which it uses as reference to the pro noun.
In this scenario, if the system when constructing a summary considers the second sentence and forgets about the first one, the semantics of that whole sentence are lost .This problem is not only found in this study but it is huge problem in the field of automatic text summarization. This s part of our future work.
ACKNOWLEDGEMENT
This work is based on the research undertaken within the Telkom CoE in ICTD supported in part by Telkom SA, Tellabs, Saab Grintek Technologies, Easttel and Khula Holdings, THRIP, GRMDC and National Research Foundation of South Africa (UID: 86108). The opinions, findings and conclusions or recommendations expressed here are those of authors and none of the above sponsors accepts any liability whatsoever in this regard.
