This blog is a gentle introduction to text summarization and can serve as a practical summary of the current landscape. It describes how we, a team of three students in the RaRe Incubator programme, have experimented with existing algorithms and Python tools in this domain.
We compare modern extractive methods like LexRank, LSA, Luhn and Gensim’s existing TextRank summarization module on the Opinosis dataset of 51 article-summary pairs. We also had a try with an abstractive technique using Tensorflow’s Text Summarization algorithm, but didn’t obtain good results due to its extremely high hardware demands (7000 GPU hours, ~$30k cloud credits).
With push notifications and article digests gaining more and more traction, the task of generating intelligent and accurate summaries for long pieces of text has become a popular research as well as industry problem.
There are two fundamental approaches to text summarization: extractive and abstractive. The former extracts words and word phrases from
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