FENICE: Factuality Evaluation of Summarization Based on Natural Language Inference and Claim Extraction

Concetti Chiave

The author introduces FENICE, a novel metric for factuality evaluation in summarization, leveraging NLI-based alignments between claims extracted from the summary and the source text at multiple levels of granularities.

Sintesi

FENICE addresses the challenge of factual inconsistencies in automatically generated summaries. It proposes an interpretable metric that aligns claims with information from the source document, setting a new benchmark for factuality evaluation. The metric is efficient, accurate, and applicable to long-form summarization.

Recent advancements in text summarization have led to high performance but also raised concerns about factual inconsistencies. FENICE aims to overcome these challenges by providing an interpretable and efficient factuality-oriented metric. By aligning claims with atomic facts extracted from the summary, FENICE achieves superior accuracy compared to existing metrics.

The approach involves extracting atomic facts (claims) from the summary and aligning them with specific sections of the input document using NLI-based alignments. This strategy enhances interpretability by highlighting relevant sections for claim verification or identifying hallucinations. FENICE outperforms other metrics on standard benchmarks and excels in evaluating long-form summarization factuality through human annotation processes.

In experiments, FENICE demonstrates state-of-the-art performance on AGGREFACT datasets, showcasing its effectiveness in evaluating both standard and long-form summarization factuality. The metric's adaptability to different text granularities ensures robustness and accuracy in assessing factual consistency across various types of documents.

Statistiche

"FENICE achieves the highest average result in the AggreFact benchmark."
"Our metric sets a new state of the art on AGGREFACT."
"FENICE leverages an NLI-based alignment between information in the source document and a set of atomic facts."

Citazioni

"The inclusion of reliable automatic metrics that detect factual inaccuracies in summaries is becoming increasingly urgent."
"Our approach differs from previous ones by solely utilizing an LLM for claim extraction."
"FENICE achieves superior accuracy with respect to its competitors."

Approfondimenti chiave tratti da

FENICE

by Ales... alle arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.02270.pdf

Domande più approfondite

How can FENICE's alignment strategy be further optimized for different types of documents?

FENICE's alignment strategy can be optimized for different types of documents by considering the specific characteristics and requirements of each document type. For example:

Long-form Texts: For longer documents, the alignment strategy could focus on aligning claims with larger sections of text, such as paragraphs or even chapters, to capture the context necessary for verifying claims accurately.
Scientific Papers: In scientific papers, where precise information is crucial, the alignment strategy could prioritize aligning technical terms and key concepts between the summary and source document.
Legal Documents: Legal texts often contain complex language and terminology; optimizing FENICE's alignment for legal documents may involve incorporating domain-specific knowledge bases or dictionaries to improve accuracy.

To optimize FENICE's alignment strategy effectively, it would be beneficial to conduct domain-specific research and tailor the alignment process based on the unique characteristics of each document type.

What are potential limitations or biases introduced by relying on NLI-based alignments for factuality evaluation?

Relying solely on NLI-based alignments for factuality evaluation may introduce several limitations and biases:

Semantic Understanding: NLI models may not always capture nuanced semantic relationships accurately, leading to misalignments between claims in summaries and corresponding information in source documents.
Domain Specificity: NLI models trained on general datasets may struggle with domain-specific terminology or contexts present in specialized texts like medical reports or legal documents.
Cultural Biases: NLI models might reflect cultural biases present in their training data, potentially affecting how they interpret statements within summaries from diverse cultural backgrounds.
Ambiguity Handling: Ambiguous phrases or contextually dependent statements could lead to incorrect alignments if not handled appropriately by the NLI model.

To mitigate these limitations and biases when using NLI-based alignments for factuality evaluation, it is essential to combine them with other techniques like manual verification processes or incorporate additional contextual cues specific to certain domains.

How might incorporating multilingual models impact FENICE's performance across diverse languages?

Incorporating multilingual models into FENICE could have several impacts on its performance across diverse languages:

Improved Language Coverage: Multilingual models can handle multiple languages simultaneously, enhancing FENICE's applicability in a global context where evaluations need to be conducted across various languages.
Cross-Lingual Alignment : Multilingual models enable cross-lingual understanding which can aid in aligning claims from summaries written in one language with source texts written in another language accurately.
Language-Specific Nuances : By leveraging multilingual capabilities, FENICE can better account for language-specific nuances that affect factuality assessment differently across languages.
4 .Training Data Availability : The availability of pre-trained multilingual models allows easier adaptation of FENICE to new languages without requiring extensive training data collection efforts.

However , challenges such as bias amplification due to imbalances in training data distribution among languages should also be considered when incorporating multilingual models into FENICE’s framework .