metadata

tags:
  - summarization
  - summary
  - booksum
  - long-document
  - long-form
license: apache-2.0
datasets:
  - kmfoda/booksum
metrics:
  - rouge
widget:
  - text: >-
      large earthquakes along a given fault segment do not occur at random
      intervals because it takes time to accumulate the strain energy for the
      rupture. The rates at which tectonic plates move and accumulate strain at
      their boundaries are approximately uniform. Therefore, in first
      approximation, one may expect that large ruptures of the same fault
      segment will occur at approximately constant time intervals. If subsequent
      main shocks have different amounts of slip across the fault, then the
      recurrence time may vary, and the basic idea of periodic mainshocks must
      be modified. For great plate boundary ruptures the length and slip often
      vary by a factor of 2. Along the southern segment of the San Andreas fault
      the recurrence interval is 145 years with variations of several decades.
      The smaller the standard deviation of the average recurrence interval, the
      more specific could be the long term prediction of a future mainshock.
    example_title: earthquakes
  - text: ' A typical feed-forward neural field algorithm. Spatiotemporal coordinates are fed into a neural network that predicts values in the reconstructed domain. Then, this domain is mapped to the sensor domain where sensor measurements are available as supervision. Class and Section Problems Addressed Generalization (Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid Representations (Section 3) Computation & memory efficiency, representation capacity, editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section 5) Spectral bias, integration & derivatives. Manipulating Neural Fields (Section 6) Edit ability, constraints, regularization. Table 2: The five classes of techniques in the neural field toolbox each addresses problems that arise in learning, inference, and control. (Section 3). We can supervise reconstruction via differentiable forward maps that transform Or project our domain (e.g, 3D reconstruction via 2D images; Section 4) With appropriate network architecture choices, we can overcome neural network spectral biases (blurriness) and efficiently compute derivatives and integrals (Section 5). Finally, we can manipulate neural fields to add constraints and regularizations, and to achieve editable representations (Section 6). Collectively, these classes constitute a ''toolbox'' of techniques to help solve problems with neural fields There are three components in a conditional neural field: (1) An encoder or inference function € that outputs the conditioning latent variable 2 given an observation 0 E(0) =2. 2 is typically a low-dimensional vector, and is often referred to aS a latent code Or feature code_ (2) A mapping function 4 between Z and neural field parameters O: Y(z) = O; (3) The neural field itself $. The encoder € finds the most probable z given the observations O: argmaxz P(2/0). The decoder maximizes the inverse conditional probability to find the most probable 0 given Z: arg- max P(Olz). We discuss different encoding schemes with different optimality guarantees (Section 2.1.1), both global and local conditioning (Section 2.1.2), and different mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable prior over the sur- face in its reconstruction domain to generalize to the partial observations. A neural network expresses a prior via the function space of its architecture and parameters 0, and generalization is influenced by the inductive bias of this function space (Section 5).'
    example_title: scientific paper
  - text: ' the big variety of data coming from diverse sources is one of the key properties of the big data phenomenon. It is, therefore, beneficial to understand how data is generated in various environments and scenarios, before looking at what should be done with this data and how to design the best possible architecture to accomplish this The evolution of IT architectures, described in Chapter 2, means that the data is no longer processed by a few big monolith systems, but rather by a group of services In parallel to the processing layer, the underlying data storage has also changed and became more distributed This, in turn, required a significant paradigm shift as the traditional approach to transactions (ACID) could no longer be supported. On top of this, cloud computing is becoming a major approach with the benefits of reducing costs and providing on-demand scalability but at the same time introducing concerns about privacy, data ownership, etc In the meantime the Internet continues its exponential growth: Every day both structured and unstructured data is published and available for processing: To achieve competitive advantage companies have to relate their corporate resources to external services, e.g. financial markets, weather forecasts, social media, etc While several of the sites provide some sort of API to access the data in a more orderly fashion; countless sources require advanced web mining and Natural Language Processing (NLP) processing techniques: Advances in science push researchers to construct new instruments for observing the universe O conducting experiments to understand even better the laws of physics and other domains. Every year humans have at their disposal new telescopes, space probes, particle accelerators, etc These instruments generate huge streams of data, which need to be stored and analyzed. The constant drive for efficiency in the industry motivates the introduction of new automation techniques and process optimization: This could not be done without analyzing the precise data that describe these processes. As more and more human tasks are automated, machines provide rich data sets, which can be analyzed in real-time to drive efficiency to new levels. Finally, it is now evident that the growth of the Internet of Things is becoming a major source of data. More and more of the devices are equipped with significant computational power and can generate a continuous data stream from their sensors. In the subsequent sections of this chapter, we will look at the domains described above to see what they generate in terms of data sets. We will compare the volumes but will also look at what is characteristic and important from their respective points of view. 3.1 The Internet is undoubtedly the largest database ever created by humans. While several well described; cleaned, and structured data sets have been made available through this medium, most of the resources are of an ambiguous, unstructured, incomplete or even erroneous nature. Still, several examples in the areas such as opinion mining, social media analysis, e-governance, etc, clearly show the potential lying in these resources. Those who can successfully mine and interpret the Internet data can gain unique insight and competitive advantage in their business An important area of data analytics on the edge of corporate IT and the Internet is Web Analytics.'
    example_title: data science textbook
  - text: >-
      Transformer-based models have shown to be very useful for many NLP tasks.
      However, a major limitation of transformers-based models is its O(n^2)O(n 
      2) time & memory complexity (where nn is sequence length). Hence, it's
      computationally very expensive to apply transformer-based models on long
      sequences n > 512n>512. Several recent papers, e.g. Longformer, Performer,
      Reformer, Clustered attention try to remedy this problem by approximating
      the full attention matrix. You can checkout 🤗's recent blog post in case
      you are unfamiliar with these models.

      BigBird (introduced in paper) is one of such recent models to address this
      issue. BigBird relies on block sparse attention instead of normal
      attention (i.e. BERT's attention) and can handle sequences up to a length
      of 4096 at a much lower computational cost compared to BERT. It has
      achieved SOTA on various tasks involving very long sequences such as long
      documents summarization, question-answering with long contexts.

      BigBird RoBERTa-like model is now available in 🤗Transformers. The goal of
      this post is to give the reader an in-depth understanding of big bird
      implementation & ease one's life in using BigBird with 🤗Transformers.
      But, before going into more depth, it is important to remember that the
      BigBird's attention is an approximation of BERT's full attention and
      therefore does not strive to be better than BERT's full attention, but
      rather to be more efficient. It simply allows to apply transformer-based
      models to much longer sequences since BERT's quadratic memory requirement
      quickly becomes unbearable. Simply put, if we would have ∞ compute & ∞
      time, BERT's attention would be preferred over block sparse attention
      (which we are going to discuss in this post).

      If you wonder why we need more compute when working with longer sequences,
      this blog post is just right for you!

      Some of the main questions one might have when working with standard
      BERT-like attention include:

      Do all tokens really have to attend to all other tokens? Why not compute
      attention only over important tokens? How to decide what tokens are
      important? How to attend to just a few tokens in a very efficient way? In
      this blog post, we will try to answer those questions.

      What tokens should be attended to? We will give a practical example of how
      attention works by considering the sentence 'BigBird is now available in
      HuggingFace for extractive question answering'. In BERT-like attention,
      every word would simply attend to all other tokens.

      Let's think about a sensible choice of key tokens that a queried token
      actually only should attend to by writing some pseudo-code. Will will
      assume that the token available is queried and build a sensible list of
      key tokens to attend to.

      >>> # let's consider following sentence as an example >>> example =
      ['BigBird', 'is', 'now', 'available', 'in', 'HuggingFace', 'for',
      'extractive', 'question', 'answering']

      >>> # further let's assume, we're trying to understand the representation
      of 'available' i.e.  >>> query_token = 'available' >>> # We will
      initialize an empty `set` and fill up the tokens of our interest as we
      proceed in this section. >>> key_tokens = [] # => currently 'available'
      token doesn't have anything to attend Nearby tokens should be important
      because, in a sentence (sequence of words), the current word is highly
      dependent on neighboring past & future tokens. This intuition is the idea
      behind the concept of sliding attention.
    example_title: bigbird blog intro
  - text: >-
      To be fair, you have to have a very high IQ to understand Rick and Morty.
      The humour is extremely subtle, and without a solid grasp of theoretical
      physics most of the jokes will go over a typical viewer's head. There's
      also Rick's nihilistic outlook, which is deftly woven into his
      characterisation- his personal philosophy draws heavily from Narodnaya
      Volya literature, for instance. The fans understand this stuff; they have
      the intellectual capacity to truly appreciate the depths of these jokes,
      to realise that they're not just funny- they say something deep about
      LIFE. As a consequence people who dislike Rick & Morty truly ARE idiots-
      of course they wouldn't appreciate, for instance, the humour in Rick's
      existential catchphrase 'Wubba Lubba Dub Dub,' which itself is a cryptic
      reference to Turgenev's Russian epic Fathers and Sons. I'm smirking right
      now just imagining one of those addlepated simpletons scratching their
      heads in confusion as Dan Harmon's genius wit unfolds itself on their
      television screens. What fools.. how I pity them. 😂

      And yes, by the way, i DO have a Rick & Morty tattoo. And no, you cannot
      see it. It's for the ladies' eyes only- and even then they have to
      demonstrate that they're within 5 IQ points of my own (preferably lower)
      beforehand. Nothin personnel kid 😎
    example_title: Richard & Mortimer
parameters:
  max_length: 64
  min_length: 8
  no_repeat_ngram_size: 3
  early_stopping: true
  repetition_penalty: 3.5
  length_penalty: 0.3
  encoder_no_repeat_ngram_size: 3
  num_beams: 4

long-t5-tglobal-base-16384-booksum

summarize long text and get a SparkNotes-esque summary of arbitrary topics!
generalizes reasonably well to academic & narrative text.

Cheeky Proof-of-Concept

A summary of the infamous navy seals copypasta:

The narrator tells the audience that he can kill anyone anywhere in the world with his bare hands, and he has access to all of the United States military's weapons.

Model description

A fine-tuned version of google/long-t5-tglobal-base on the kmfoda/booksum dataset:

20+ epochs of fine-tuning from the base model on V100/A100 GPUs
all training used 16384 token input / 1024 max output

Read the paper by Guo et al. here: LongT5: Efficient Text-To-Text Transformer for Long Sequences

How-To in Python

Install/update transformers pip install -U transformers

Summarize text with pipeline:

from transformers import pipeline

summarizer = pipeline(
    'summarization',
    'pszemraj/long-t5-tglobal-base-16384-book-summary',
    )
long_text = "Here is a lot of text I don't want to read. Replace me"

result = summarizer(long_text)
print(result[0]['summary_text'])

Pass other parameters related to beam search textgen when calling summarizer to get even higher quality results.

Intended uses & limitations

At the time of writing, the model is not fully converged despite training for 20+ epochs. This checkpoint is serviceable enough (see examples).
- I plan to update this page with newer checkpoints and post some metrics over time.
- Compare performance to LED-base trained on the same dataset (API gen parameters are the same).
while this model seems to improve upon factual consistency, do not take summaries to be foolproof and check things that seem odd.

Training and evaluation data

kmfoda/booksum dataset. Summaries longer than 1024 LongT5 tokens were filtered out with the intent of preventing the model from learning to generate "partial" summaries.

early checkpoints of this model were trained on a "smaller" subsection of the dataset as it was filtered for summaries of 1024 characters. This was subsequently caught and adjusted to 1024 tokens and then trained further for at least five epochs.

Training procedure

Training hyperparameters

The following hyperparameters were used during the final training round*:

learning_rate: 0.0004
train_batch_size: 2
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 64
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.02
num_epochs: 2

*Prior training sessions used roughly similar parameters; multiple sessions were required as this takes eons to train

Training results

Framework versions

Transformers 4.20.1
Pytorch 1.10.0+cu113
Datasets 2.3.2
Tokenizers 0.12.1