  In recent years, several influential computational models and metrics have
been proposed to predict how humans comprehend and process sentence. One
particularly promising approach is contextual semantic similarity. Inspired by
the attention algorithm in Transformer and human memory mechanisms, this study
proposes an ``attention-aware'' approach for computing contextual semantic
relevance. This new approach takes into account the different contributions of
contextual parts and the expectation effect, allowing it to incorporate
contextual information fully. The attention-aware approach also facilitates the
simulation of existing reading models and evaluate them. The resulting
``attention-aware'' metrics of semantic relevance can more accurately predict
fixation durations in Chinese reading tasks recorded in an eye-tracking corpus
than those calculated by existing approaches. The study's findings further
provide strong support for the presence of semantic preview benefits in Chinese
naturalistic reading. Furthermore, the attention-aware metrics of semantic
relevance, being memory-based, possess high interpretability from both
linguistic and cognitive standpoints, making them a valuable computational tool
for modeling eye-movements in reading and further gaining insight into the
process of language comprehension. Our approach underscores the potential of
these metrics to advance our comprehension of how humans understand and process
language, ultimately leading to a better understanding of language
comprehension and processing.
