# Can Artificial Intelligence Write Like [PERSON]? An Evaluation Protocol for Spanish Microfiction
## 2 Microfiction and Evaluation Methods
### How Has Text Generation Been Evaluated?
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Author** & **Goal** & **Approach** & **Evaluation Method** & **Results** \\ \hline [PERSON] et al., 2021 [24] & Short text generation for an image & Deep Learning & None & Generation of short texts using an image and encoder-decoder structure. \\ \hline [PERSON] et al., 2022 [25] & Poem generation using GPT-2 & Deep Learning & Lexical diversity, Subject continuity, BERT-based Embedding Distances, WordNet-based Similarity Metric, Content Classification & Limerick poems with AABBA rhyming scheme. \\ \hline [PERSON] et al., 2002 [26] & Character-based interactive story generation & Rule-based & Quantification of system’s generative potential & Computer entertainment story generation. \\ \hline [PERSON] et al. 2005 [27] & Story that matches a given query & Ontology-based & None & Sketch of a story plot. \\ \hline [PERSON] et al., 2019 [28] & Story generation with better story endings & Neural Network-based & Human evaluation & Endings containing positive emotions, supported by sentiment analysis. \\ \hline [PERSON] et al., 2013 [29] & Story generation & Symbolic Encoding & Levenshtein Distance, BLEU & Stories and fables. \\ \hline [PERSON] et al., 2009 [30] & Annotation tool for the semantic encoding of texts & Symbolic Encoding & Human evaluation & Short fables. \\ \hline [PERSON] et al., 2011 [31] & Character-level language modeling & Neural Network-based & Bits Per Character & Text generation with gated RNNs. \\ \hline [PERSON] et al., 2016 [32] & To generate an output by dynamically adjusting interpolation among a language model and attention models & Neural Network-based BLEU-4, METEOR & Human evaluation, BLEU-4, METEOR & Text generation with global coherence. \\ \hline [PERSON] et al., 2015 [33] & Rich descriptive explanations and alignment between book and movie & Neural Network-based BLEU, TF-IDF & Story generation from images and texts. \\ \hline [PERSON] et al., 2011 [34] & Generate story dialogues from film characters & Statistical Model & Human evaluation & Dialogues generated based on given film characters. \\ \hline [PERSON] et al., 2016 [35] & Generation of a film script & Neural Network-based & None & Short film script. \\ \hline [PERSON] et al., 2017 [36] & Sentence planning & Neural Network-based BLEU & Levenshtein Distance, BLEU & Parameterized sentence planner. \\ \hline [PERSON] et al., 2024 [4] & Poem generation using ChatGPT & Deep Learning & Human (crowd evaluation with Prolific) & Evaluators preferred ChatGPT poems to those from well known human authors. \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._discourse coherence by employing deep learning (DL) techniques. Their evaluation metrics included perplexity, bilingual evaluation understudy (BLEU) [37], lexical and semantic repetition, distinct-4, context relatedness, and sentence order, which collectively assess the model's ability to maintain logical flow and coherence. Shorter-form text generation has also been explored, with [24] proposing an encoder-decoder structure for generating short texts based on images while optimizing for succinctness and relevance. Notably, this approach lacked an explicit evaluation mechanism. Meanwhile, ref. [25] utilized GPT-2 [38] to generate structured poetry adhering to specific poetic constraints such as the AABBA rhyming scheme of limerick poems. The effectiveness of this approach was evaluated using lexical diversity, subject continuity, BERT-based embedding distances [39], WordNet-based similarity metrics, and content classification. In addition to traditional story generation, several studies have focused on interactive and rule-based systems. In [26], the authors introduced a character-based interactive storytelling mechanism aimed at enhancing computer entertainment evaluated through a quantification of the system's generative potential. In [27], an ontology-based system was employed to generate plots matching specific user queries, although without specifying an evaluation mechanism.