Abstract:
Text Summary is a technique of producing short, fluent, and most significantly correct precis
of a respectively longer textual content. There are two broad categories of methods to textual
content summary: extraction method and abstraction method. The MDS is a system through
which the content of many documents is summarized. We investigated the way of designing
and developing multi-document summarization for AO using RNN and the way of tackling the
problem of MDS for AO. The researcher used supervised machine learning, to train on
manually created summaries by humans. We present the extractive MDS for AO using DL for
which we used long short-term memory encoder-decoder with ROUGE evaluation metrics.
For experimentation, 1800 datasets were used. During experiment, seven different
scenarios were considered primarily based on the different epochs and batch size. The
ROUGE consists of measures to robotically decide the best summary through evaluating it to
reference summaries created by humans. It measures the quantity of overlapping units
including both the n-gram and word pairs among system generated. This research achieved a
performance of ROUGE-1 R = 0.27, P = 0.24 and f1-score= 0.26 and the ROUGE-2 R = 0.037,
P = 0.034 and f1-score = 0.035. This work indicated that a LSTM algorithm was good and
applicable to Afaan Oromo.
