Summary

pipeline pipeline

The Summary pipeline summarizes text. This pipeline runs a text2text model that abstractively creates a summary of the input text.

Example

The following shows a simple example using this pipeline.

  1. from txtai.pipeline import Summary
  3. # Create and run pipeline
  4. summary = Summary()
  5. summary("Enter long, detailed text to summarize here")

See the link below for a more detailed example.

NotebookDescription
Building abstractive text summariesRun abstractive text summarizationOpen In Colab

Configuration-driven example

Pipelines are run with Python or configuration. Pipelines can be instantiated in configuration using the lower case name of the pipeline. Configuration-driven pipelines are run with workflows or the API.

config.yml

  1. # Create pipeline using lower case class name
  2. summary:
  4. # Run pipeline with workflow
  5. workflow:
  6.   summary:
  7.     tasks:
  8.       - action: summary

Run with Workflows

  1. from txtai import Application
  3. # Create and run pipeline with workflow
  4. app = Application("config.yml")
  5. list(app.workflow("summary", ["Enter long, detailed text to summarize here"]))

Run with API

  1. CONFIG=config.yml uvicorn "txtai.api:app" &
  3. curl \
  4.   -X POST "http://localhost:8000/workflow" \
  5.   -H "Content-Type: application/json" \
  6.   -d '{"name":"summary", "elements":["Enter long, detailed text to summarize here"]}'

Methods

Python documentation for the pipeline.

__init__(path=None, quantize=False, gpu=True, model=None, **kwargs)

Source code in txtai/pipeline/text/summary.py

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  1. def init(self, path=None, quantize=False, gpu=True, model=None, kwargs):
  2.     super().init(“summarization”, path, quantize, gpu, model, kwargs)

__call__(text, minlength=None, maxlength=None, workers=0)

Runs a summarization model against a block of text.

This method supports text as a string or a list. If the input is a string, the return type is text. If text is a list, a list of text is returned with a row per block of text.

Parameters:

NameTypeDescriptionDefault
text

text|list

required
minlength

minimum length for summary

None
maxlength

maximum length for summary

None
workers

number of concurrent workers to use for processing data, defaults to None

0

Returns:

TypeDescription

summary text

Source code in txtai/pipeline/text/summary.py

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  1. def call(self, text, minlength=None, maxlength=None, workers=0):
  2.     “””
  3.     Runs a summarization model against a block of text.
  5.     This method supports text as a string or a list. If the input is a string, the return
  6.     type is text. If text is a list, a list of text is returned with a row per block of text.
  8.     Args:
  9.         text: text|list
  10.         minlength: minimum length for summary
  11.         maxlength: maximum length for summary
  12.         workers: number of concurrent workers to use for processing data, defaults to None
  14.     Returns:
  15.         summary text
  16.     “””
  18.     # Validate text length greater than max length
  19.     check = maxlength if maxlength else self.maxlength()
  21.     # Skip text shorter than max length
  22.     texts = text if isinstance(text, list) else [text]
  23.     params = [(x, text if len(text) >= check else None) for x, text in enumerate(texts)]
  25.     # Build keyword arguments
  26.     kwargs = self.args(minlength, maxlength)
  28.     inputs = [text for _, text in params if text]
  29.     if inputs:
  30.         # Run summarization pipeline
  31.         results = self.pipeline(inputs, num_workers=workers, **kwargs)
  33.         # Pull out summary text
  34.         results = iter([self.clean(x[“summary_text”]) for x in results])
  35.         results = [next(results) if text else texts[x] for x, text in params]
  36.     else:
  37.         # Return original
  38.         results = texts
  40.     return results[0] if isinstance(text, str) else results