24 research outputs found

    Question-driven text summarization with extractive-abstractive frameworks

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    Automatic Text Summarisation (ATS) is becoming increasingly important due to the exponential growth of textual content on the Internet. The primary goal of an ATS system is to generate a condensed version of the key aspects in the input document while minimizing redundancy. ATS approaches are extractive, abstractive, or hybrid. The extractive approach selects the most important sentences in the input document(s) and then concatenates them to form the summary. The abstractive approach represents the input document(s) in an intermediate form and then constructs the summary using different sentences than the originals. The hybrid approach combines both the extractive and abstractive approaches. The query-based ATS selects the information that is most relevant to the initial search query. Question-driven ATS is a technique to produce concise and informative answers to specific questions using a document collection. In this thesis, a novel hybrid framework is proposed for question-driven ATS taking advantage of extractive and abstractive summarisation mechanisms. The framework consists of complementary modules that work together to generate an effective summary: (1) discovering appropriate non-redundant sentences as plausible answers using a multi-hop question answering system based on a Convolutional Neural Network (CNN), multi-head attention mechanism and reasoning process; and (2) a novel paraphrasing Generative Adversarial Network (GAN) model based on transformers rewrites the extracted sentences in an abstractive setup. In addition, a fusing mechanism is proposed for compressing the sentence pairs selected by a next sentence prediction model in the paraphrased summary. Extensive experiments on various datasets are performed, and the results show the model can outperform many question-driven and query-based baseline methods. The proposed model is adaptable to generate summaries for the questions in the closed domain and open domain. An online summariser demo is designed based on the proposed model for the industry use to process the technical text

    WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

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    Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

    ChatGPT und andere "Quatschmaschinen": Gespräche mit Künstlicher Intelligenz

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    Die Veröffentlichung von ChatGPT im Herbst 2022 heizte die Kontroverse um Künstliche Intelligenz an und führte zu einer seitdem unaufhörlichen Fragelust - verstärkt dadurch, dass dieselben Prompts schon kürzeste Zeit später andere Outputs generieren. In einem experimentellen Format präsentieren die Herausgeber*innen erste kommentierte Gespräche mit KI-Sprachmodellen. Sie geben Einblick in dialogische Szenen, die eine fortlaufende Transformation der Technik und Eigentümlichkeiten maschinellen Lernens erfassen. Die Sammlung zielt in Form witziger, unheimlicher und - mehr oder weniger - kluger Dialoge zwischen Mensch und Maschine auf die Dokumentation einer mediengeschichtlichen Passage in eine neue Ära allgegenwärtiger KI

    Ground Truth Spanish Automatic Extractive Text Summarization Bounds

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    The textual information has accelerated growth in the most spoken languages by native Internet users, such as Chinese, Spanish, English, Arabic, Hindi, Portuguese, Bengali, Russian, among others. It is necessary to innovate the methods of Automatic Text Summarization (ATS) that can extract essential information without reading the entire text. The most competent methods are Extractive ATS (EATS) that extract essential parts of the document (sentences, phrases, or paragraphs) to compose a summary. During the last 60 years of research of EATS, the creation of standard corpus with human-generated summaries and evaluation methods which are highly correlated with human judgments help to increase the number of new state-of-the-art methods. However, these methods are mainly supported for the English language, leaving aside other equally important languages such as Spanish, which is the second most spoken language by natives and the third most used on the Internet. A standard corpus for Spanish EATS (SAETS) is created to evaluate the state-of-the-art methods and systems for the Spanish language. The main contribution consists of a proposal for configuration and evaluation of 5 state-ofthe-art methods, five systems and four heuristics using three evaluation methods (ROUGE, ROUGE-C, and Jensen-Shannon divergence). It is the first time that Jensen-Shannon divergence is used to evaluate AETS. In this paper the ground truth bounds for the Spanish language are presented, which are the heuristics baseline:first, baseline:random, topline and concordance. In addition, the ranking of 30 evaluation tests of the state-of-the-art methods and systems is calculated that forms a benchmark for SAETS

    Towards More Human-Like Text Summarization: Story Abstraction Using Discourse Structure and Semantic Information.

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    PhD ThesisWith the massive amount of textual data being produced every day, the ability to effectively summarise text documents is becoming increasingly important. Automatic text summarization entails the selection and generalisation of the most salient points of a text in order to produce a summary. Approaches to automatic text summarization can fall into one of two categories: abstractive or extractive approaches. Extractive approaches involve the selection and concatenation of spans of text from a given document. Research in automatic text summarization began with extractive approaches, scoring and selecting sentences based on the frequency and proximity of words. In contrast, abstractive approaches are based on a process of interpretation, semantic representation, and generalisation. This is closer to the processes that psycholinguistics tells us that humans perform when reading, remembering and summarizing. However in the sixty years since its inception, the field has largely remained focused on extractive approaches. This thesis aims to answer the following questions. Does knowledge about the discourse structure of a text aid the recognition of summary-worthy content? If so, which specific aspects of discourse structure provide the greatest benefit? Can this structural information be used to produce abstractive summaries, and are these more informative than extractive summaries? To thoroughly examine these questions, they are each considered in isolation, and as a whole, on the basis of both manual and automatic annotations of texts. Manual annotations facilitate an investigation into the upper bounds of what can be achieved by the approach described in this thesis. Results based on automatic annotations show how this same approach is impacted by the current performance of imperfect preprocessing steps, and indicate its feasibility. Extractive approaches to summarization are intrinsically limited by the surface text of the input document, in terms of both content selection and summary generation. Beginning with a motivation for moving away from these commonly used methods of producing summaries, I set out my methodology for a more human-like approach to automatic summarization which examines the benefits of using discourse-structural information. The potential benefit of this is twofold: moving away from a reliance on the wording of a text in order to detect important content, and generating concise summaries that are independent of the input text. The importance of discourse structure to signal key textual material has previously been recognised, however it has seen little applied use in the field of autovii matic summarization. A consideration of evaluation metrics also features significantly in the proposed methodology. These play a role in both preprocessing steps and in the evaluation of the final summary product. I provide evidence which indicates a disparity between the performance of coreference resolution systems as indicated by their standard evaluation metrics, and their performance in extrinsic tasks. Additionally, I point out a range of problems for the most commonly used metric, ROUGE, and suggest that at present summary evaluation should not be automated. To illustrate the general solutions proposed to the questions raised in this thesis, I use Russian Folk Tales as an example domain. This genre of text has been studied in depth and, most importantly, it has a rich narrative structure that has been recorded in detail. The rules of this formalism are suitable for the narrative structure reasoning system presented as part of this thesis. The specific discourse-structural elements considered cover the narrative structure of a text, coreference information, and the story-roles fulfilled by different characters. The proposed narrative structure reasoning system produces highlevel interpretations of a text according to the rules of a given formalism. For the example domain of Russian Folktales, a system is implemented which constructs such interpretations of a tale according to an existing set of rules and restrictions. I discuss how this process of detecting narrative structure can be transferred to other genres, and a key factor in the success of this process: how constrained are the rules of the formalism. The system enumerates all possible interpretations according to a set of constraints, meaning a less restricted rule set leads to a greater number of interpretations. For the example domain, sentence level discourse-structural annotations are then used to predict summary-worthy content. The results of this study are analysed in three parts. First, I examine the relative utility of individual discourse features and provide a qualitative discussion of these results. Second, the predictive abilities of these features are compared when they are manually annotated to when they are annotated with varying degrees of automation. Third, these results are compared to the predictive capabilities of classic extractive algorithms. I show that discourse features can be used to more accurately predict summary-worthy content than classic extractive algorithms. This holds true for automatically obtained annotations, but with a much clearer difference when using manual annotations. The classifiers learned in the prediction of summary-worthy sentences are subsequently used to inform the production of both extractive and abstractive summaries to a given length. A human-based evaluation is used to compare these summaries, as well as the outputs of a classic extractive summarizer. I analyse the impact of knowledge about discourse structure, obtained both manually and automatically, on summary production. This allows for some insight into the knock on effects on summary production that can occur from inaccurate discourse information (narrative structure and coreference information). My analyses show that even given inaccurate discourse information, the resulting abstractive summaries are considered more informative than their extractive counterparts. With human-level knowledge about discourse structure, these results are even clearer. In conclusion, this research provides a framework which can be used to detect the narrative structure of a text, and shows its potential to provide a more human-like approach to automatic summarization. I show the limit of what is achievable with this approach both when manual annotations are obtainable, and when only automatic annotations are feasible. Nevertheless, this thesis supports the suggestion that the future of summarization lies with abstractive and not extractive techniques

    Automatic Structured Text Summarization with Concept Maps

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    Efficiently exploring a collection of text documents in order to answer a complex question is a challenge that many people face. As abundant information on almost any topic is electronically available nowadays, supporting tools are needed to ensure that people can profit from the information's availability rather than suffer from the information overload. Structured summaries can help in this situation: They can be used to provide a concise overview of the contents of a document collection, they can reveal interesting relationships and they can be used as a navigation structure to further explore the documents. A concept map, which is a graph representing concepts and their relationships, is a specific form of a structured summary that offers these benefits. However, despite its appealing properties, only a limited amount of research has studied how concept maps can be automatically created to summarize documents. Automating that task is challenging and requires a variety of text processing techniques including information extraction, coreference resolution and summarization. The goal of this thesis is to better understand these challenges and to develop computational models that can address them. As a first contribution, this thesis lays the necessary ground for comparable research on computational models for concept map--based summarization. We propose a precise definition of the task together with suitable evaluation protocols and carry out experimental comparisons of previously proposed methods. As a result, we point out limitations of existing methods and gaps that have to be closed to successfully create summary concept maps. Towards that end, we also release a new benchmark corpus for the task that has been created with a novel, scalable crowdsourcing strategy. Furthermore, we propose new techniques for several subtasks of creating summary concept maps. First, we introduce the usage of predicate-argument analysis for the extraction of concept and relation mentions, which greatly simplifies the development of extraction methods. Second, we demonstrate that a predicate-argument analysis tool can be ported from English to German with low effort, indicating that the extraction technique can also be applied to other languages. We further propose to group concept mentions using pairwise classifications and set partitioning, which significantly improves the quality of the created summary concept maps. We show similar improvements for a new supervised importance estimation model and an optimal subgraph selection procedure. By combining these techniques in a pipeline, we establish a new state-of-the-art for the summarization task. Additionally, we study the use of neural networks to model the summarization problem as a single end-to-end task. While such approaches are not yet competitive with pipeline-based approaches, we report several experiments that illustrate the challenges - mostly related to training data - that currently limit the performance of this technique. We conclude the thesis by presenting a prototype system that demonstrates the use of automatically generated summary concept maps in practice and by pointing out promising directions for future research on the topic of this thesis

    Tune your brown clustering, please

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    Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal
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