# Can Artificial Intelligence Write Like [PERSON]? An Evaluation Protocol for Spanish Microfiction
## 2 Microfiction and Evaluation Methods
### How Has Text Generation Been Evaluated?
Further refining narrative outcomes, ref. [28] investigated the impact of sentiment-driven story endings, leveraging neural networks to generate positive emotional resolutions. Their evaluation relied on human judgment to assess the effectiveness of emotional storytelling. Similarly, ref. [29] developed a symbolic encoding method for automated story and fable creation, which they evaluated by using Levenshtein distance and BLEU scores to measure textual similarity and fluency. Annotation tools also play a role in text generation research, as exemplified by [30], who introduced a semantic encoding system designed for textual annotation. Their study was evaluated using human assessment to verify the quality of semantic encodings. In a different vein, ref. [31] explored character-level language modeling with gated recurrent neural networks (RNNs) to improve text synthesis at a granular level, employing bits per character as the primary evaluation metric. Efforts in coherence and interpolation-based generation are represented by [32], who proposed a model that dynamically adjusts interpolation between a language model and attention mechanisms to maintain global coherence. Their evaluation relied on human judgment as well as BLEU-4 and METEOR (Metric for Evaluation of Translation with Explicit Ordering) scores to assess linguistic accuracy and coherence. Additionally, ref. [33] investigated alignment techniques for rich descriptive explanations, aiming to generate textual content that effectively bridges books and their movie adaptations. This approach was evaluated using BLEU and term frequency-inverse document frequency (TF-IDF) similarity measures. Several researchers have instead focused on dialogue-based storytelling. In [34], the authors developed a statistical model for generating film dialogues based on character archetypes while ensuring that the generated dialogues maintain consistency with established personas. Evaluation relied on human assessments of the generated dialogues. Expanding upon this, ref. [35] explored automated scriptwriting using neural networks, resulting in the creation of a short film script; however, no explicit evaluation mechanism was reported. Other approaches such as [36] have delved into sentence planning techniques, employing neural network-based methodologies to refine parameterized sentence structure generation. In this instance, the evaluation used Levenshtein distance and BLEU scores to measure textual structure and fluency. Lastly, a critical stand is needed concerning recent research results from [4] suggesting that average readers prefer AI-generated poetry to classical texts. While the statistical rigor and scale of this study are noteworthy, its interpretive claims are weakened by a fundamental oversight, namely, the absence of literary concepts, particularly the theory of reception [10; 11]. By relying exclusively on a crowd-sourced evaluation platform populated by self-reported non-experts (90.4% of whom read poetry infrequently and two-thirds of whom were unfamiliar with the assigned poets), the authors equated statistical significance with literary insight. Reception theory emphasizes that meaning is co-produced by readers, whose interpretive frameworks are shaped by their literary competence, cultural background, and historical awareness [19; 20]. Thus, the study's conclusion that AI-generated poems are preferred over canonical works rests on a narrow understanding of preference that neglects the interpretive depth and aesthetic endurance that define literary reception. Without accounting for the respondents' literary literacy, the finding that AI poems are judged to be superior risks privileging superficial readability over poetic complexity, potentially reframing the appreciation of literature in terms of immediate appeal rather than interpretive depth and enduring value.