| Step | Tool / Technique | What It Does | |------|------------------|--------------| | | pdfminer.six , PyMuPDF , or Adobe Acrobat’s “Export to Word” | Turns the PDF into plain text or a structured format (JSON, XML). | | b. Pre‑processing | NLTK, spaCy, or Hugging Face tokenizers | Sentence segmentation, stop‑word removal, lemmatization, handling of math symbols or tables. | | c. Feature engineering | • TF‑IDF vectors (scikit‑learn) • Word embeddings (Word2Vec, GloVe) • Contextual embeddings (BERT, RoBERTa, SciBERT) | Produces numeric representations of words, sentences, or whole sections. | | d. Topic modeling / clustering | LDA, BERTopic, HDBSCAN | Identifies the main themes or “deep features” that run through the document. | | e. Semantic similarity / citation mapping | Sentence‑BERT, SPECTER (for scientific papers) | Lets you compare sections of the PDF to each other or to external literature. | | f. Visualization | pyLDAvis, t‑SNE / UMAP plots, network graphs (NetworkX) | Makes the extracted features interpretable. | | g. Summarization | Extractive (TextRank) or abstractive (BART, PEGASUS) models | Generates concise overviews of each major feature or section. |
This depends on your stage of life.