# Evaluation of Automatic Text Summarization using Synthetic Facts

**Authors:** Jay Ahn, Foaad Khosmood  
**arXiv:** [2204.04869](https://arxiv.org/abs/2204.04869)  
**Date:** 2022-04-11  
**Summary page:** https://www.summarizepaper.com/en/paper/2204.04869v1/

## Key points

- Recent advancements in the realm of text summarization
- Challenges still exist in terms of reliability and practical application
- Main issues with current methods: inconsistency and subjectivity in human-generated summaries
- Difficulty in ensuring generated summaries accurately reflect facts from the source text
- Introduction of a new automatic reference-less text summarization evaluation system
- Focus on factual consistency, comprehensiveness, and compression rate
- Uses synthetic documents with known facts for evaluation
- Ability to interpret and extract facts from ambiguous or partial information within text

## Summary

In the realm of , recent advancements have been made, but challenges still exist in terms of reliability and practical application. The main issues with current methods lie in  and . Traditional metrics rely on human-generated summaries, which can be inconsistent and subjective. Additionally, ensuring that generated summaries accurately reflect the facts present in the source text remains a significant challenge. To address these shortcomings, a new automatic reference-less text summarization evaluation system has been proposed. This innovative system aims to measure the quality of any text summarization model by focusing on factual consistency, comprehensiveness, and compression rate. By generating synthetic documents containing known facts, this system provides a controlled environment for evaluating the accuracy of summarization outputs. One notable strength of this evaluation system is its ability to interpret and extract facts from ambiguous or partial information within text. For instance, it can identify relationships between entities mentioned in different sentences and consider partial facts when assessing the quality of a summary.