Summarizing More Than PDFs: Extracting Text From EPUB, PPTX, and Images in Python

Everyone's PDF summarizer demo uses a clean, text-based PDF. Then real files show up: an EPUB e-book, a .pptx deck, a folder of scanned JPGs. Each needs a different extractor before you can send anything to an LLM. Here's a practical, format-by-format guide to getting clean text out of messy documents in Python.

The rule of thumb: the summary is only as good as the text you extract. Garbage in, garbage summary.

The general pipeline

detect format → extract text (format-specific) → normalize → chunk → summarize

The only step that changes per format is extraction. Let's cover the four that trip people up.

PDFs (the baseline)

Native PDFs are easy with pypdf:

from pypdf import PdfReader

def from_pdf(path: str) -> str:
    reader = PdfReader(path)
    return "\n".join((page.extract_text() or "") for page in reader.pages)

If this returns almost nothing, the PDF is scanned — jump to the images section for OCR.

EPUB e-books

EPUB is basically zipped XHTML. Use ebooklib to walk the documents and BeautifulSoup to strip tags:

# pip install EbookLib beautifulsoup4
from ebooklib import epub, ITEM_DOCUMENT
from bs4 import BeautifulSoup

def from_epub(path: str) -> str:
    book = epub.read_epub(path)
    chapters = []
    for item in book.get_items_of_type(ITEM_DOCUMENT):
        soup = BeautifulSoup(item.get_content(), "html.parser")
        chapters.append(soup.get_text(separator="\n"))
    return "\n\n".join(chapters)

Gotcha: books are long. Don't send a whole novel in one call — chunk it and use map-reduce, or summarize chapter by chapter (each XHTML item is usually a chapter).

PowerPoint (.pptx)

The trap with decks is that meaning hides in speaker notes, not just slide text. python-pptx reads both:

# pip install python-pptx
from pptx import Presentation

def from_pptx(path: str) -> str:
    prs = Presentation(path)
    out = []
    for i, slide in enumerate(prs.slides, start=1):
        out.append(f"--- Slide {i} ---")
        for shape in slide.shapes:
            if shape.has_text_frame:
                out.append(shape.text_frame.text)
        # speaker notes — the part most tools skip
        if slide.has_notes_slide:
            notes = slide.notes_slide.notes_text_frame.text
            if notes.strip():
                out.append(f"[Notes] {notes}")
    return "\n".join(out)

Note what this won't get: text baked into images or charts on a slide. For those you'd need OCR on a rendered slide image.

Images and scanned pages (OCR)

For JPG/PNG or image-only PDFs, run OCR with Tesseract:

# pip install pytesseract pillow  (needs the tesseract binary installed)
import pytesseract
from PIL import Image

def from_image(path: str) -> str:
    return pytesseract.image_to_string(Image.open(path))

OCR output is noisy — collapse repeated whitespace and expect the odd misread before you summarize.

Wiring it together

from pathlib import Path

EXTRACTORS = {
    ".pdf": from_pdf,
    ".epub": from_epub,
    ".pptx": from_pptx,
    ".jpg": from_image,
    ".jpeg": from_image,
    ".png": from_image,
}

def extract(path: str) -> str:
    ext = Path(path).suffix.lower()
    if ext not in EXTRACTORS:
        raise ValueError(f"No extractor for {ext}")
    text = EXTRACTORS[ext](path)
    return " ".join(text.split())  # normalize whitespace

Now extract() feeds the same chunk → summarize step regardless of format. Add your LLM call of choice after this.

Effort vs. payoff

Format	Library	Main gotcha
PDF (native)	pypdf	Empty output = scanned, needs OCR
EPUB	EbookLib + BeautifulSoup	Very long; chunk by chapter
PPTX	python-pptx	Don't forget speaker notes; misses image text
Images / scans	pytesseract	Noisy output; install tesseract binary

When it's not worth maintaining

If format support is core to your product, build and own the extractors above. But if you occasionally need to summarize a random EPUB or deck, maintaining four libraries (plus a Tesseract install) to reinvent a file upload is a lot. A free no-code tool that already accepts all these formats is the pragmatic escape hatch: ChatPDF and NotebookLM handle common docs if you have an account, and PDFSummarizer.net takes 14 formats — PDF, EPUB/FB2/MOBI, PPTX, images and more — with no sign-up. The developer catch: it's a browser tool with no public API, so it can't go in a pipeline. Build the extractors when you need automation; use a ready tool when you don't.

Takeaways

• Extraction, not the LLM, is where document summarization actually breaks.
• Each format needs its own extractor: pypdf, EbookLib, python-pptx, pytesseract.
• For decks, always grab the speaker notes.
• Normalize whitespace before chunking.
• Don't maintain four parsers for an occasional job — that's what no-code tools are for.

What formats have given you the most trouble? Drop your extraction horror stories in the comments.

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Tool details were accurate at the time of writing — check current limits before you rely on them.