## 2 Materials and Methods
### Feature Extraction  
First, each sentence of the stimulus text and corresponding response was encoded to numerical representations using sentence embedding. For the embedding models, term frequency-inverse document frequency (TF-IDF) [10] and Sentence BERT (SBERT) [15; 16] were used. The main motivation for choosing these two embedding models was to compare the word-frequency-based and contextualized embeddings. TF-IDF is calculated from the frequencies of words and how insensitive their arrangement is. In contrast, SBERT is the Transformer-based embedding that considers neighboring words using the attention mechanism [15; 16].  
For TF-IDF, the vocabulary was built by first normalizing the dataset and extracting nouns, similarly to previous studies [18; 19], using the open-source Korean text processor [20]. Nouns that appeared more than once in the dataset were collected to form the vocabulary size of 246.  
Using TF-IDF, stimulus sentences and readers' responses were encoded to 246 dimensional vectors, with each dimension representing the frequency of the corresponding noun. Specifically, the TF-IDF value is defined by the product of the term frequency (TF) and inverse document frequency (IDF) as follows:  
\[\begin{array}{c}\text{TF-IDF}(\text{w},t)=\text{TF}(\text{w},t)\text{IDF}( \text{w})\\ \text{IDF}(\text{w})=\text{log}\frac{1+\text{N}}{1+\text{DF}(\text{w})}+1 \end{array} \tag{1}\]  
where TF(w,t) is the term frequency of the given term w in the sentence t, N is the total number of sentences, and DF(w) is the number of sentences that contain term w. The resulting TF-IDF vectors were normalized using the Euclidean norm.  
SBERT is a variation in the Transformer architecture [21] tailored for sentence-level representations. Employing the Siamese network structure [15; 16], the SBERT model learns sentence embeddings that have smaller distances for semantically similar sentences. To train an SBERT model, sentence pairs with annotated semantic similarity scores are used. This is in contrast to other Transformer models that learn numerical representations by learning to predict masked words based on the surrounding context.  
Sentence embedding by SBERT was performed as follows. Following the preprocessing method for the Korean Language Understanding Evaluation (KLUE) benchmark dataset [22], each sentence and its corresponding response were tokenized into morphemes, and byte pair encoding was applied with the vocabulary size of 32 K. A pre-trained SBERT model with RoBERTa architecture [23] for Korean was obtained from [24], which was trained with the KorNLU dataset [25] provided by KakaoBrain [26]. Using this model, 768 dimensional embedding vectors were calculated for all the sentences in the stimulus text and readers' responses.  
Next, similarity scores between the embeddings of the stimulus sentences and the corresponding responses were calculated as follows. For each sentence and corresponding response, embedding vectors are called \(v_{s}\) and \(v_{r}\), respectively. The cosine similarity score between these embeddings, called \(s(v_{s},v_{r})\), is calculated as follows.  
\[s(v_{s},v_{r})=\frac{v_{s}\cdot v_{r}}{\left|v_{s}\right|\left|v_{r}\right|} \tag{2}\]where \(\cdot\) denotes the inner product of two vectors and \(|\ |\) represents the Euclidean norm. Because there were 10 sentences in the stimulus text, 10 similarity scores were computed for each participant.