Generating summaries with HuggingFace summarization models#

2023 Dec 6

Can I use one of the summarization models published on HuggingFace to locally generate summaries for each page of my Sphinx site? The answer after one night of Covid-delirious experimentation appears to be a resounding “maybe”!

Summarization is attractive to me for a few reasons. By “attractive” I mean I may actually try to ship page summarization on the docs sites I work on.

It’s actually useful. A lot of technical docs use Too. Many. Damn. Words. Summaries are very helpful for making quick information scent decisions.
It can be architectified as a batch job. Generate the summary at build time and store it somewhere. You don’t need to re-generate until the page content changes.

HuggingFace Transformers is super cool. The API is really beginner-friendly and the docs site seems really well done. It took me a comically long time to realize that I’m supposed to mentally replace every instance of “🤗” in their docs with “HuggingFace” but once I got past that hiccup I was cruising.

My other site, biodigitaljazz.net, runs on Sphinx. I spun up a simple extension to generate a summary for each page. First, let’s look at an even simpler extension that just spits out the text of a single page:

import docutils

def show_me_the_text(app, doctree, docname):
    if docname != 'blog/picam':
        return
    text = doctree.astext()
    print(text)

def setup(app):
    app.connect('doctree-resolved', show_me_the_text)
    return {
        'parallel_read_safe': True,
        'parallel_write_safe': True,
    }

There are some setup steps I’m skipping but after it’s all rigged up it prints out the following text:

The eleventh circle of hell: setting up an RPi camera module

2023 Nov 22

Short story long, don’t trust any of the community content; it’s full of ghosts
and wolverines and those people you meet at parties that start the
conversation with “so what do you do?”… Rely solely on the official RPi
docs. And make sure that whatever you’re reading was written for the
specific HW/SW permutation that you’re using. In my case (RPi4 + RPi OS
Bookworm + RPi Camera Module 3) the correct doc is The Picamera2 Library.

Once I found the right doc it was trivial to get everything working. The
picamera2 repo has an extensive collection of examples. It’s downright magical
that I can get started with computer vision in a matter of minutes. OpenCV is
really cool stuff.

This gem of a quote from an RPi engineer sums up the situation:

Hi, yes it’s a bit of a minefield out there on the web because so much
content is still referring to the legacy camera stack which will never
(for example) support the camera module 3, or even work at all on any
reasonably modern Raspberry Pi OS image.

I’m not writing this post to complain; who has time for that? This is just
a courtesy heads up to my fellow hackers.

<p style="margin-top: 10000px;">
    Huh? You're still here? And you scrolled all the way down here just
    to ask "What's the tenth circle?" Excellent question. I have no idea.
</p>

Every model sees exactly this same text. Now let’s do some summarizing!

import docutils
import transformers

models = [
    'facebook/bart-large-cnn',
    'sshleifer/distilbart-cnn-12-6',
    'philschmid/bart-large-cnn-samsum',
    'pszemraj/led-base-book-summary',
    'google/pegasus-cnn_dailymail',
    'google/pegasus-xsum',
    'Falconsai/medical_summarization',
    'google/bigbird-pegasus-large-arxiv',
    'knkarthick/MEETING_SUMMARY',
    'pszemraj/led-large-book-summary',
    'google/pegasus-large',
    'knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI',
    'facebook/bart-large-xsum',
    'csebuetnlp/mT5_multilingual_XLSum',
    'ArtifactAI/led_large_16384_arxiv_summarization'
]

def summarize(app, doctree, docname):
    if docname != 'blog/picam':
        return
    text = doctree.astext()
    for model in models:
        print(f'\n\n\n********** {model} **********\n\n')
        try:
            summarizer = transformers.pipeline('summarization', model=model)
            result = summarizer(text)
            print(result[0]['summary_text'])
        except Exception as e:
            print('error')

def setup(app):
    app.connect('doctree-resolved', summarize)
    return {
        'parallel_read_safe': True,
        'parallel_write_safe': True,
    }

I pulled that list of models from the most downloaded models that support the summarization pipeline.

And now the results!

********** facebook/bart-large-cnn **********

vern totality totality totalityグググflagsググ Fieldsググlysググ derivativeググ
78グ Fieldslysグ Fields Fields Fieldsグlys Fieldsグ Fields scathing Fields
Fieldslys Fields Fields ne Fields Fields 78 Fields Fields derivative Fields
Fields scathingグ Fields nelys Fieldslyslys Fields ne ne Fieldslys ne Fields
scathing ne Fields ne Fif Fields FieldsAMES Fields Fields Excel Fields Fields
Reeves Fields Fields Fif Fields Fiflys Fields Fif ne Fields Fif Fif Fif
Fieldslys Fif Fif ne Fif Fiflys Fif Fieldsouk Fif Fifoukoukouk
Charlottesvilleouk LauderSnapouk LauderReviewReview Lauder Lauder Lauder
Contrast Lauder Lauder brewing profiles brewing brewing brewing Lauder brewing
Contrast Contrast Contrast brewing brewing Registration brewing brewing matching
brewing brewing Contrast brewing

Holy shit, talk about a terrible start. I have no idea what scathingグ means but I definitely feel threatened by it. Let’s just put that one in the vault and keep going…

(Of course, the real answer is that I’m just holding something wrong or the model isn’t actually intended for this task. Never underestimate pilot error.)

********** sshleifer/distilbart-cnn-12-6 **********

error

********** philschmid/bart-large-cnn-samsum **********

error

********** pszemraj/led-base-book-summary **********

The eleventh circle of hell: setting up an RPi camera module In this chapter,
the author explains how to get everything working without relying on any of
the official RPi docs. He uses OpenCV and OpenCV to get things running. This is
just a courtesy post to his fellow hackers.

OK! 4 models in and we’ve got something almost usable. The formatting is off though and there’s actually a huge factual error. It says I got everything working without relying on any of the official docs. That is categorically not what I say in the post.

(A lot of the models error’d. I think that’s just because I’m running these tests via transformers[tf-cpu] on a Linux.)

Onwards! No prisoners! No prisoners!

********** google/pegasus-cnn_dailymail **********

error

********** google/pegasus-xsum **********

All images are copyrighted.

What in the sam hell. That text doesn’t even appear on my page.

********** Falconsai/medical_summarization **********

the eleventh circle of hell: setting up an RPi camera module 2023 Nov 22 Short
story long , don’t trust any of the community content; it’s full of ghosts and
wolverines and those people you meet at parties that start the conversation with
“so what do you do?”... Rely solely on the official docs . once I found the
right doc it was trivial to get everything working . the picamera2 repo has an
extensive collection of examples .

********** google/bigbird-pegasus-large-arxiv **********

error

********** knkarthick/MEETING_SUMMARY **********

Setting up a camera module on a Raspberry Pi is a daunting task. The correct
documentation can be found in The Picamera2 Library. OpenCV is really cool.

Not bad! I might be able to actually use this.

********** pszemraj/led-large-book-summary **********

Bookworm + RPi OS = nightmare #1: setting up an RPi camera module Warning: The
community site is full of ghost and wolverines and people asking "so what do you
do?” Do not trust the community site. Instead, read the official RPi docs. In my
case, the correct doc is The Picamera2 Library. Once I found the right doc, it
was trivial to get everything working. It's downright magical that I can get
started with computer vision in a matter of minutes. OpenCV is really cool
stuff. This gem of a quote sums up the situation:Hi, yes it's a minefield out
there on the web because so much content is still referring to the legacy camera
stack which will never support the camera module 3, or even work at all on any
reasonably modern Raspberry Pi OS image.I'm not writing this post to complain;
who has time for that? This is just a courtesy heads up to my fellow hackers.

A lot of these things seem to just be verbatim regurgitations of my original text, with worse formatting. Bookworm + RPi OS = nightmare has a nice ring to it, though…

********** google/pegasus-large **********

error

********** knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI **********

The eleventh circle of hell: setting up an RPi camera module. The correct doc is
The Picamera2 Library. OpenCV is cool stuff.

This is a variant of the other model that worked well. This text is a little better than that previous model.

********** facebook/bart-large-xsum **********

I’m a big fan of OpenCV, but I’ve found that if you want to get started with
computer vision on a Raspberry Pi it’s best to stick to the official
documentation.

What stands out to me most is how different this text is than the other models. It’s sending a negative message about OpenCV that doesn’t exist in the source text, though.

********** csebuetnlp/mT5_multilingual_XLSum **********

error

********** ArtifactAI/led_large_16384_arxiv_summarization **********

the eleventh circle of hell: setting up an RPi camera module

Well, that about does ‘er. Wraps ‘er all up. I’ll get those errors fixed and keep exploring other models. I’ll also try more sophisticated model invocations, such as the example from csebuetnlp/mT5_multilingual_XLSum.

Assuming that I can find a model that works, the next hurdle is the size of the dependencies. The transformers[tf-cpu] library that I used appears to be hundreds of megabytes. The model is usually another gigabyte at least. And of course there’s all the licensing uncertainty around generated content. I also imagine that these models can’t handle that much text. I guess I could do a divide-and-conquer approach where I generate a summary for each section, concatenate all those summaries together, and then run the summarization one last time on the concatenated section-level summaries. Sphinx makes it trivial to process a doc section-by-section. Sphinx rules.

P.S. the next time you find yourself battling an evil wizard, remember the auric defense incantation: Fields scathingグ Fields nelys Fieldslyslys Fields ne ne Fieldslys ne Fields