Frequency and TextRank Summarizer for Plain Text Files

Reading long documents end to end is often unnecessary when a handful of key sentences carries the substance, and building a summarizer is one of the most satisfying first NLP projects because the output is immediately judgeable: either the summary captures the document or it does not. It is also an ideal introduction to the idea that text can be treated as data. The intern builds a beginner-friendly Python command-line tool that summarizes plain text files using two classical extractive techniques. The frequency-based method uses NLTK to tokenize the document, remove stopwords, score words by occurrence, and rank sentences by the importance of the words they contain. The TextRank method goes further, constructing a graph with NetworkX where sentences are nodes connected by similarity, then ranking them with an iterative algorithm inspired by how search engines rank web pages. The CLI accepts an input file and desired summary length, prints the selected sentences in original order, and lets users compare what each method chose. The project introduces the intern to tokenization, stopword handling, and graph-based ranking in a single approachable build, and produces a genuinely usable utility that demonstrates they can transform an algorithm description into working, well-organized Python code.