How to Use FactSumm: Your Guide to Scoring Factual Consistency in Abstractive Summarization

Oct 21, 2020 | Data Science

In the realm of natural language processing, maintaining factual consistency in summarization models is akin to preserving the essence and integrity of a story when telling it in your own words. Meet FactSumm, a toolkit specifically designed to score Factual Consistency for Abstractive Summarization. In this guide, we will walk you through the installation, usage, and troubleshooting of this powerful toolkit.

Installation

Getting started with FactSumm is straightforward. You need to have Python 3.8 or later installed on your computer. You can choose to install it using pip or clone the source repository. Here are your options:

  • To install via pip, run the following command in your terminal:
  • pip install factsumm
  • Alternatively, if you’d like to install from the source, use the following commands:
  • git clone https://github.com/huffon/factsumm
    cd factsumm
    pip install .

Usage

Once you have FactSumm installed, using it is as easy as pie! Think of it similar to having a personal editor who checks whether every important detail from the source material has been accurately captured in the summary, just like an editor would verify facts in a news article.

Here’s a snippet of how you can start scoring factual consistency:

from factsumm import FactSumm
factsumm = FactSumm()

article = "Lionel Andrés Messi (born 24 June 1987) is an Argentine professional footballer..."
summary = "Lionel Andrés Messi (born 24 Aug 1997) is a Spanish professional footballer..."

factsumm(article, summary, verbose=True)

Understanding the Output

The output generated will include:

  • Source Entities: A breakdown of entities extracted from the original article.
  • Summary Entities: A list of entities from your summary.
  • Common Facts: Facts that match in both the source and the summary.
  • Diff Facts: Facts that differ between the source and summary.
  • Fact Score: A numerical score representing the factual consistency.

Analyzing the differences, much like comparing a final draft with the original manuscript, can help you pinpoint where the summary deviates from the source material.

Advanced Scoring Modules

FactSumm comes equipped with several advanced modules robust enough for deeper analysis:

  • Triple-Based Module: This counts overlapping fact triples.
  • QA-Based Module: Tests consistency through questions generated from the summary.
  • ROUGE-Based Module: Measures word-level overlaps for similarity assessment.
  • BERTScore Module: Calculates sentence similarity using BERT embeddings.

Troubleshooting Tips

If you encounter issues while using FactSumm, consider these troubleshooting steps:

  • Ensure you have the correct version of Python installed.
  • Check if the syntax of your input strings is correct.
  • Make sure all necessary dependencies are installed. Refer to the installation guide if needed.
  • If the scores seem off, revisit your source and summary for inconsistencies or errors.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

References

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