Recent work on text simplification has focused on the use of control tokens to further the state-of-the-art. However, it is not easy to further improve without an in-depth comprehension of the mechanisms underlying control tokens. One unexplored factor is the tokenization strategy, which we also explore. In this paper, we (1) reimplemented AudienCe-CEntric Sentence Simplification, (2) explored the effects and interactions of varying control tokens, (3) tested the influences of different tokenization strategies, (4) demonstrated how separate control tokens affect performance and (5) proposed new methods to predict the value of control tokens. We show variations of performance in the four control tokens separately. We also uncover how the design of control tokens could influence performance and give some suggestions for designing control tokens. We show the newly proposed method with higher performance in both SARI (a common scoring metric in text simplificaiton) and BERTScore (a score derived from the BERT language model) and potential in real applications.

This article discusses the development of an automated plot extraction system for narrative texts. Acknowledging the distinction between plot, as an object of study with its own rich history and literature, and features of a text that may be automatically extractable, we begin by characterizing a text’s scatter plot of entities. This visualization of a text reveals entity density patterns characterizing the particular telling of the story under investigation and leads to effective scene partitioning. We then introduce the concept of narrative flow, a graph representation of the narrative ordering of scenes (the syuzhet) that includes how entities move through scenes from the text, and investigate the degree to which narrative flow can be automatically extracted given a glossary of plot-important objects, actors, and locations. Our subsequent analysis then explores the correlation between subjective notions of plot and the information extracted through these visualizations. In particular, we discuss narrative structures commonly found within the graphs and make comparisons with ground truth narrative flow graphs, showing mixed results highlighting the difficulty of plot extraction. However, the visual artifacts and common structural relationships seen in the graphs provide insight into narrative and its underlying plot.

Reading comprehension and fluency are crucial for successful academic learning and achievement. Yet, a rather large percentage of children still have enormous difficulties in understanding a written text at the end of primary school. In this context, the aim of our study was to investigate whether text simplification, a process of reducing text complexity while keeping its meaning unchanged, can improve reading fluency and comprehension for children learning to read. Furthermore, we were interested in finding out whether some readers would benefit more than others from text simplification as a function of their cognitive and language profile. To address these issues, we developed an iBook application for iPads, which allowed us to present normal and simplified versions of informative and narrative texts to 165 children in grade 2. Reading fluency was measured for each sentence, and text comprehension was measured for each text using multiple-choice questions. The results showed that both reading fluency and reading comprehension were significantly better for simplified than for normal texts. Results showed that poor readers and children with weaker cognitive skills (nonverbal intelligence, memory) benefitted to a greater extent from simplification than good readers and children with somewhat stronger cognitive skills.

Technical writing in professional environments, such as user manual authoring, requires the use of uniform language. Nonuniform language refers to sentences in a technical document that are intended to have the same meaning within a similar context, but use different words or writing style. Addressing this nonuniformity problem requires the performance of two tasks. The first task, which we named nonuniform language detection (NLD), is detecting such sentences. We propose an NLD method that utilizes different similarity algorithms at lexical, syntactic, semantic and pragmatic levels. Different features are extracted and integrated by applying a machine learning classification method. The second task, which we named nonuniform language correction (NLC), is deciding which sentence among the detected ones is more appropriate for that context. To address this problem, we propose an NLC method that combines contraction removal, near-synonym choice, and text readability comparison. We tested our methods using smartphone user manuals. We finally compared our methods against state-of-the-art methods in paraphrase detection (for NLD) and against expert annotators (for both NLD and NLC). The experiments demonstrate that the proposed methods achieve performance that matches expert annotators.