  Text summarization is an approach for identifying important information
present within text documents. This computational technique aims to generate
shorter versions of the source text, by including only the relevant and salient
information present within the source text. In this paper, we propose a novel
method to summarize a text document by clustering its contents based on latent
topics produced using topic modeling techniques and by generating extractive
summaries for each of the identified text clusters. All extractive
sub-summaries are later combined to generate a summary for any given source
document. We utilize the lesser used and challenging WikiHow dataset in our
approach to text summarization. This dataset is unlike the commonly used news
datasets which are available for text summarization. The well-known news
datasets present their most important information in the first few lines of
their source texts, which make their summarization a lesser challenging task
when compared to summarizing the WikiHow dataset. Contrary to these news
datasets, the documents in the WikiHow dataset are written using a generalized
approach and have lesser abstractedness and higher compression ratio, thus
proposing a greater challenge to generate summaries. A lot of the current
state-of-the-art text summarization techniques tend to eliminate important
information present in source documents in the favor of brevity. Our proposed
technique aims to capture all the varied information present in source
documents. Although the dataset proved challenging, after performing extensive
tests within our experimental setup, we have discovered that our model produces
encouraging ROUGE results and summaries when compared to the other published
extractive and abstractive text summarization models.
