Linear Text Segmentation is the task of automatically tagging text documents with topic shifts, i.e. the places in the text where the topics change. A well-established area of research in Natural Language Processing, drawing from well-understood concepts in linguistic and computational linguistic research, the field has recently seen a lot of interest as a result of the surge of text, video, and audio available on the web, which in turn require ways of summarising and categorizing the mole of content for which linear text segmentation is a fundamental step. In this survey, we provide an extensive overview of current advances in linear text segmentation, describing the state of the art in terms of resources and approaches for the task. Finally, we highlight the limitations of available resources and of the task itself, while indicating ways forward based on the most recent literature and under-explored research directions.

In this paper we propose a new framework and new methods for the reference-free evaluation of topic segmentation systems directly in the embedding space. Specifically, we define a common framework for reference-free, embedding-based topic segmentation metrics, and show how this applies to an existing metric. We then define new metrics, based on a previously defined cohesion score, Average Relative Proximity. Using this approach, we show that Large Language Models (LLMs) yield features that, if used correctly, can strongly correlate with traditional topic segmentation metrics based on costly and rare human annotations, while outperforming existing reference-free metrics borrowed from clustering evaluation in most domains. We then show that smaller language models specifically fine-tuned for different sentence-level tasks can outperform LLMs several orders of magnitude larger. Via a thorough comparison of our metric’s performance across different datasets, we see that conversational data present the biggest challenge in this framework. Finally, we analyse the behaviour of our metrics in specific error cases, such as those of under-generation and moving of ground truth topic boundaries, and show that our metrics behave more consistently than other reference-free methods.

Recent works on linear text segmentation have shown new state-of-the-art results nearly every year. Most times, however, these recent advances include a variety of different elements which makes it difficult to evaluate which individual components of the proposed methods bring about improvements for the task and, more generally, what actually works for linear text segmentation. Moreover, evaluating text segmentation is notoriously difficult and the use of a metric such as Pk, which is widely used in existing literature, presents specific problems that complicates a fair comparison between segmentation models. In this work, then, we draw from a number of existing works to assess which is the state-of-the-art in linear text segmentation, investigating what architectures and features work best for the task. For doing so, we present three models representative of a variety of approaches, we compare them to existing methods and we inspect elements composing them, so as to give a more complete picture of which technique is more successful and why that might be the case. At the same time, we highlight a specific feature of Pk which can bias the results and we report our results using different settings, so as to give future literature a more comprehensive set of baseline results for future developments. We then hope that this work can serve as a solid foundation to foster research in the area, overcoming task-specific difficulties such as evaluation setting and providing new state-of-the-art results.

Due to the scarcity of high-quality bilingual sentence pairs, some deep-learning-based machine translation algorithms cannot achieve better performance in low-resource machine translation. On this basis, we are committed to integrating the ideas of machine learning algorithm improvement and data augmentation, propose a novel multiloop incremental bootstrapping framework, and design the corresponding semi-supervised learning algorithm. This framework is a meta-frame independent of specific machine translation algorithms. This algorithm makes full use of bilingual seed data of appropriate scale and super-large-scale monolingual data to expand bilingual sentence pair data incrementally, and trains machine translation models step by step to improve the translation quality. The experimental results of neural machine translation on multiple language pairs prove that our proposed framework can make use of continuous monolingual data to raise itself. Its effectiveness is not only reflected in the easy implementation of state-of-the-art low-resource machine translation, but also in the practical option to quickly establish precise domain machine translation systems.

This paper proposes a sentiment-centric pipeline to perform unsupervised plot extraction on non-linear novels like Virginia Woolf’s Mrs. Dalloway, a novel widely considered to be “plotless. Combining transformer-based sentiment analysis models with statistical testing, we model sentiment’s rate-of-change and correspondingly segment the novel into emotionally self-contained units qualitatively evaluated to be meaningful surrogate pseudo-chapters. We validate our findings by evaluating our pipeline as a fully unsupervised text segmentation model, achieving a F-1 score of 0.643 (regional) and 0.214 (exact) in chapter break prediction on a validation set of linear novels with existing chapter structures. In addition, we observe notable differences between the distributions of predicted chapter lengths in linear and non-linear fictional narratives, with the latter exhibiting significantly greater variability. Our results hold significance for narrative researchers appraising methods for extracting plots from non-linear novels.

Story generation is a challenging task of automatically creating natural languages to describe a sequence of events, which requires outputting text with not only a consistent topic but also novel wordings. Although many approaches have been proposed and obvious progress has been made on this task, there is still a large room for improvement, especially for improving thematic consistency and wording diversity. To mitigate the gap between generated stories and those written by human writers, in this paper, we propose a planning-based conditional variational autoencoder, namely Plan-CVAE, which first plans a keyword sequence and then generates a story based on the keyword sequence. In our method, the keywords planning strategy is used to improve thematic consistency while the CVAE module allows enhancing wording diversity. Experimental results on a benchmark dataset confirm that our proposed method can generate stories with both thematic consistency and wording novelty, and outperforms state-of-the-art methods on both automatic metrics and human evaluations.

Vietnamese word segmentation (VWS) is a challenging basic issue for natural language processing. This paper addresses the problem of how does dictionary size influence VWS performance, proposes two novel measures: square overlap ratio (SOR) and relaxed square overlap ratio (RSOR), and validates their effectiveness. The SOR measure is the product of dictionary overlap ratio and corpus overlap ratio, and the RSOR measure is the relaxed version of SOR measure under an unsupervised condition. The two measures both indicate the suitable degree between segmentation dictionary and object corpus waiting for segmentation. The experimental results show that the more suitable, neither smaller nor larger, dictionary size is better to achieve the state-of-the-art performance for dictionary-based Vietnamese word segmenters.