Analysis of GraphSum’s Attention Weights to Improve the Explainability of Multi-Document Summarization

Hickmann, Maya; Wurzberger, Fabian; Hoxhalli, Megi; Lochner, Arne; Töllich, Jessica; Scherp, Ansgar

doi:10.1145/3487664.3487788

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Attention-based Methods1

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2021

2024

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Cited by 3 publications

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“…Humphreys et al proposed an architecture for predicting defects, using the sum of attention weights over all layers [76]. Attention weights were employed for searching in a transformer-based model dedicated to multi-document summarization in [77].…”

Section: Attention-based Methodsmentioning

confidence: 99%

The Explainability of Transformers: Current Status and Directions

Fantozzi,

Naldi

2024

Computers

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An increasing demand for model explainability has accompanied the widespread adoption of transformers in various fields of applications. In this paper, we conduct a survey of the existing literature on the explainability of transformers. We provide a taxonomy of methods based on the combination of transformer components that are leveraged to arrive at the explanation. For each method, we describe its mechanism and survey its applications. We find out that attention-based methods, both alone and in conjunction with activation-based and gradient-based methods, are the most employed ones. A growing attention is also devoted to the deployment of visualization techniques to help the explanation process.

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Section: Attention-based Methodsmentioning

confidence: 99%