txtai - Summary (original) (raw)
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.
`from txtai.pipeline import Summary
Create and run pipeline
summary = Summary() summary("Enter long, detailed text to summarize here") `
See the link below for a more detailed example.
| Notebook | Description | |
|---|---|---|
| Building abstractive text summaries | Run abstractive text summarization |  |
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
`# Create pipeline using lower case class name summary:
Run pipeline with workflow
workflow: summary: tasks: - action: summary `
Run with Workflows
`from txtai import Application
Create and run pipeline with workflow
app = Application("config.yml") list(app.workflow("summary", ["Enter long, detailed text to summarize here"])) `
Run with API
`CONFIG=config.yml uvicorn "txtai.api:app" &
curl
-X POST "http://localhost:8000/workflow"
-H "Content-Type: application/json"
-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
| def __init__(self, path=None, quantize=False, gpu=True, model=None, **kwargs): super().__init__("summarization", path, quantize, gpu, model, **kwargs) |
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__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:
| Name | Type | Description | Default |
|---|---|---|---|
| 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:
| Type | Description |
|---|---|
| summary text |
Source code in txtai/pipeline/text/summary.py
| 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | def __call__(self, 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. Args: text: text|list minlength: minimum length for summary maxlength: maximum length for summary workers: number of concurrent workers to use for processing data, defaults to None Returns: summary text """ # Validate text length greater than max length check = maxlength if maxlength else self.maxlength() # Skip text shorter than max length texts = text if isinstance(text, list) else [text] params = [(x, text if len(text) >= check else None) for x, text in enumerate(texts)] # Build keyword arguments kwargs = self.args(minlength, maxlength) inputs = [text for _, text in params if text] if inputs: # Run summarization pipeline results = self.pipeline(inputs, num_workers=workers, **kwargs) # Pull out summary text results = iter([self.clean(x["summary_text"]) for x in results]) results = [next(results) if text else texts[x] for x, text in params] else: # Return original results = texts return results[0] if isinstance(text, str) else results |
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