Extractive Text Summarization Using Generalized Additive Models with Interactions for Sentence Selection
This work addresses the need for interpretable models in automatic text summarization, which is incremental as it adapts existing interpretable methods to a specific domain problem.
The authors tackled the problem of interpretable extractive text summarization by applying Generalized Additive Models with interactions, such as Explainable Boosting Machine and GAMI-Net, to sentence selection based on linguistic features, resulting in models that maintain interpretability while addressing the challenge of opaque dense models in the field.
Automatic Text Summarization (ATS) is becoming relevant with the growth of textual data; however, with the popularization of public large-scale datasets, some recent machine learning approaches have focused on dense models and architectures that, despite producing notable results, usually turn out in models difficult to interpret. Given the challenge behind interpretable learning-based text summarization and the importance it may have for evolving the current state of the ATS field, this work studies the application of two modern Generalized Additive Models with interactions, namely Explainable Boosting Machine and GAMI-Net, to the extractive summarization problem based on linguistic features and binary classification.