Practical Challenges in Extractive Summarization

Given the rapid pace of development in NLP, it is even more important to use compatible versions of the libraries that we use. Evaluation of a document's suitability for extractive summarization can be undertaken manually. Often, we would like to summarize multiple pieces of text, all of which could be short in length. The TextRank algorithm will not work well in such cases.

All unverified claims reported in this field ought to be taken with a grain of salt until the claim has been verified. Such claims ought to be subjected by practitioners to naïve tests such as the Little Red Riding test. We can only use a model if it works and if the limitations related to scope and any biases are considered.