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Article
Publication date: 15 August 2023

Yi-Hung Liu and Sheng-Fong Chen

Whether automatically generated summaries of health social media can assist users in appropriately managing their diseases and ensuring better communication with health…

Abstract

Purpose

Whether automatically generated summaries of health social media can assist users in appropriately managing their diseases and ensuring better communication with health professionals becomes an important issue. This paper aims to develop a novel deep learning-based summarization approach for obtaining the most informative summaries from online patient reviews accurately and effectively.

Design/methodology/approach

This paper proposes a framework to generate summaries that integrates a domain-specific pre-trained embedding model and a deep neural extractive summary approach by considering content features, text sentiment, review influence and readability features. Representative health-related summaries were identified, and user judgements were analysed.

Findings

Experimental results on the three real-world health forum data sets indicate that awarding sentences without incorporating all the adopted features leads to declining summarization performance. The proposed summarizer significantly outperformed the comparison baseline. User judgement through the questionnaire provides realistic and concrete evidence of crucial features that remarkably influence patient forum review summaries.

Originality/value

This study contributes to health analytics and management literature by exploring users’ expressions and opinions through the health deep learning summarization model. The research also developed an innovative mindset to design summarization weighting methods from user-created content on health topics.

Details

The Electronic Library , vol. 41 no. 5
Type: Research Article
ISSN: 0264-0473

Keywords

Article
Publication date: 29 November 2023

Tarun Jaiswal, Manju Pandey and Priyanka Tripathi

The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional…

Abstract

Purpose

The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.

Design/methodology/approach

In this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.

Findings

The findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.

Originality/value

This work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.

Details

Data Technologies and Applications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 13 December 2023

Sofia Martynovich

The interpretation of any emerging form or period in art history was never a trivial task. However, in the case of digital art, technology, becoming an integral part, multiplied…

Abstract

Purpose

The interpretation of any emerging form or period in art history was never a trivial task. However, in the case of digital art, technology, becoming an integral part, multiplied the complexity of describing, systematizing and evaluating it. This article investigates the most common metadata standards for the documentation of art as a broad category and suggests possible next steps toward an extended metadata standard for digital art.

Design/methodology/approach

Describing several techno-cultural phenomena formed in the last decade, manifesting the extendibility of digital art (its ability to be easily extended across multiple modalities), the article, at first, points to the long overdue need to re-evaluate the standards around it. Then it suggests a deeper analysis through a comparative study. In the scope of the study three artworks, The Arnolfini Portrait (Jan van Eyck), an iconic example of the early Renaissance, The World's First Collaborative Sentence (Douglas Davis), a classic example of early Internet art and Fake It Till You Make It (Maya Man), a prominent example of the blockchain art, are examined following the structure of the VRA Core 4.0 standard.

Findings

The comparative study demonstrates that digital art is more multi-semantic than traditional physical art, and requires new taxonomies as well as approaches for data acquisition.

Originality/value

Acknowledging that digital art simply has not yet evolved to the stage of being systematically collected by cultural institutions for documentation, curation and preservation, but otherwise, in the past few years, it has been at the front-center of social, economic and technological trends, the article suggests looking for hints on the future-proof extended metadata standard in some of those trends.

Details

Journal of Documentation, vol. 80 no. 2
Type: Research Article
ISSN: 0022-0418

Keywords

Book part
Publication date: 23 November 2023

Claudine Kuradusenge-McLeod

This chapter explores the dual, contentions spaces of consciousness the Rwandan diaspora communities navigate. First of which was created through the stories of trauma and…

Abstract

This chapter explores the dual, contentions spaces of consciousness the Rwandan diaspora communities navigate. First of which was created through the stories of trauma and displacement since the Rwandan genocide and is influenced by the current Rwandan government's control over narratives of identities and remembrance both socially and politically. The second originated from the younger generations' attempt to assimilate to the only country they have never lived in and personally known. In this second space, the younger generations were forced, consciously or unconsciously, to choose between their communities' attachment to the past or creating a new path or future. Most importantly, being in diaspora means accepting that the different generations will often remain at the periphery of the new country, like outsiders looking inward. This phenomenon of social exclusion is a result of different factors, such as social categorisation, collective trauma and the narratives of otherness, which shape the different generations' identity shifts and sense of belonging. Using a phenomenological research method, this study analysed how one event, the 1994 Rwandan genocide, changed the meaning of diaspora consciousness and divided the communities into social categories such as ‘victims’ and ‘perpetrators’. Using the experiences of Rwandan American diaspora communities, I explored the impact of the labels of ‘victim’ and ‘perpetrator’ and how they have not only created specific narratives around remembrance and accountability but also crystallised the normative ideas of who was harmed and who was responsible for inflicting that harm. This chapter analysed the Rwandan communities' social development and assimilation, their understanding of their pasts and their members' social and political engagements in addressing their roles in their communities and nations.

Details

Migrations and Diasporas
Type: Book
ISBN: 978-1-83797-147-3

Keywords

Article
Publication date: 18 May 2023

Rongen Yan, Depeng Dang, Hu Gao, Yan Wu and Wenhui Yu

Question answering (QA) answers the questions asked by people in the form of natural language. In the QA, due to the subjectivity of users, the questions they query have different…

Abstract

Purpose

Question answering (QA) answers the questions asked by people in the form of natural language. In the QA, due to the subjectivity of users, the questions they query have different expressions, which increases the difficulty of text retrieval. Therefore, the purpose of this paper is to explore new query rewriting method for QA that integrates multiple related questions (RQs) to form an optimal question. Moreover, it is important to generate a new dataset of the original query (OQ) with multiple RQs.

Design/methodology/approach

This study collects a new dataset SQuAD_extend by crawling the QA community and uses word-graph to model the collected OQs. Next, Beam search finds the best path to get the best question. To deeply represent the features of the question, pretrained model BERT is used to model sentences.

Findings

The experimental results show three outstanding findings. (1) The quality of the answers is better after adding the RQs of the OQs. (2) The word-graph that is used to model the problem and choose the optimal path is conducive to finding the best question. (3) Finally, BERT can deeply characterize the semantics of the exact problem.

Originality/value

The proposed method can use word-graph to construct multiple questions and select the optimal path for rewriting the question, and the quality of answers is better than the baseline. In practice, the research results can help guide users to clarify their query intentions and finally achieve the best answer.

Details

Data Technologies and Applications, vol. 58 no. 1
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 19 January 2024

Meng Zhu and Xiaolong Xu

Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is…

Abstract

Purpose

Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is to extract the information that is important to the intent from the input sentence. However, most of the existing methods use sentence-level intention recognition, which has the risk of error propagation, and the relationship between intention recognition and SF is not explicitly modeled. Aiming at this problem, this paper proposes a collaborative model of ID and SF for intelligent spoken language understanding called ID-SF-Fusion.

Design/methodology/approach

ID-SF-Fusion uses Bidirectional Encoder Representation from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) to extract effective word embedding and context vectors containing the whole sentence information respectively. Fusion layer is used to provide intent–slot fusion information for SF task. In this way, the relationship between ID and SF task is fully explicitly modeled. This layer takes the result of ID and slot context vectors as input to obtain the fusion information which contains both ID result and slot information. Meanwhile, to further reduce error propagation, we use word-level ID for the ID-SF-Fusion model. Finally, two tasks of ID and SF are realized by joint optimization training.

Findings

We conducted experiments on two public datasets, Airline Travel Information Systems (ATIS) and Snips. The results show that the Intent ACC score and Slot F1 score of ID-SF-Fusion on ATIS and Snips are 98.0 per cent and 95.8 per cent, respectively, and the two indicators on Snips dataset are 98.6 per cent and 96.7 per cent, respectively. These models are superior to slot-gated, SF-ID NetWork, stack-Prop and other models. In addition, ablation experiments were performed to further analyze and discuss the proposed model.

Originality/value

This paper uses word-level intent recognition and introduces intent information into the SF process, which is a significant improvement on both data sets.

Details

Data Technologies and Applications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 8 September 2023

Oussama Ayoub, Christophe Rodrigues and Nicolas Travers

This paper aims to manage the word gap in information retrieval (IR) especially for long documents belonging to specific domains. In fact, with the continuous growth of text data…

Abstract

Purpose

This paper aims to manage the word gap in information retrieval (IR) especially for long documents belonging to specific domains. In fact, with the continuous growth of text data that modern IR systems have to manage, existing solutions are needed to efficiently find the best set of documents for a given request. The words used to describe a query can differ from those used in related documents. Despite meaning closeness, nonoverlapping words are challenging for IR systems. This word gap becomes significant for long documents from specific domains.

Design/methodology/approach

To generate new words for a document, a deep learning (DL) masked language model is used to infer related words. Used DL models are pretrained on massive text data and carry common or specific domain knowledge to propose a better document representation.

Findings

The authors evaluate the approach of this study on specific IR domains with long documents to show the genericity of the proposed model and achieve encouraging results.

Originality/value

In this paper, to the best of the authors’ knowledge, an original unsupervised and modular IR system based on recent DL methods is introduced.

Details

International Journal of Web Information Systems, vol. 19 no. 5/6
Type: Research Article
ISSN: 1744-0084

Keywords

Article
Publication date: 3 November 2022

Md Moazzem Hossain, Tarek Rana, Shamsun Nahar, Md Jahidur Rahman and Aklema Choudhury Lema

The purpose of this study is to explore the sustainability reporting of a public sector organisation (PSO). This study focuses on socio-environmental practices of a major…

Abstract

Purpose

The purpose of this study is to explore the sustainability reporting of a public sector organisation (PSO). This study focuses on socio-environmental practices of a major agro-economic platform in Australia – the Murray–Darling Basin Authority (MDBA) to provide a unique perspective on water resource management and sustainability.

Design/methodology/approach

This longitudinal qualitative case study collects published data from the MDBA’s annual reports over 21 years (1998–2018) and considers economic, social and environmental dimensions of sustainability using legitimacy and institutional theory.

Findings

This study finds that the MDBA’s sustainability reporting is influenced by its response to the Water Act 2007 and the Basin Plan 2012 regulations and to maintain its legitimacy with stakeholders. The MDBA wished to pursue sustainability through integrating these regulations complemented by stakeholder expectations. Although all categories increased in reporting, the environment category has the highest primacy in achieving a healthy basin through sustainable water management for the long-term benefit of the stakeholders.

Research limitations/implications

This study contributes to the PSOs sustainability reporting literature. Particularly, this study provides insights of sustainability reporting patterns and practices over a long period through a longitudinal study. This study contributes new knowledge on the awareness of PSOs sustainability practice which has implications for governments, regulators, policymakers, managers and other stakeholders.

Originality/value

The Australian PSOs setting is under-researched from the perspective of a regulatory framework. The MDBA case provides unique insights on water resource management and sustainability which has value for many countries around the world.

Details

Meditari Accountancy Research, vol. 31 no. 5
Type: Research Article
ISSN: 2049-372X

Keywords

Book part
Publication date: 14 December 2023

Ryan Casey

The development of electronic monitoring policy over the last decade in Scotland has contributed towards its expansion and the intensification of what McNeill (2019) refers to as…

Abstract

The development of electronic monitoring policy over the last decade in Scotland has contributed towards its expansion and the intensification of what McNeill (2019) refers to as mass supervision. Often posited as a solution to relieve problems in the criminal justice system such as prison overcrowding and high remand populations, electronic monitoring can be punitive and problematic, exposing more people to diffused forms of social control and functioning more as a supplementary feature of prisons as opposed to a substitution for prisons. In this chapter, I explore the existing and emerging policy landscape of penal electronic monitoring Scotland, drawing upon qualitative, experiential data about being subject to and enforcing penal electronic monitoring in Scotland (see Casey, 2021) to highlight how policy is enacted in practice. Ultimately, I argue that there are fundamental issues with how electronic monitoring is currently enacted in terms of what it promises, in terms of fairness and in relation to the potential harms of integration. I call for a fundamental and holistic reframing of policy and regulation of penal electronic monitoring in Scotland that avoids siloed approaches towards policymaking, attending to both the social and digital impacts of electronic monitoring in people’s lives, thus contributing to arguments about how ‘mass supervision’ should be moderated and resisted.

Details

Punishment, Probation and Parole: Mapping Out ‘Mass Supervision’ In International Contexts
Type: Book
ISBN: 978-1-83753-194-3

Keywords

Book part
Publication date: 25 March 2024

Heather Yaxley

Informal conversational encounters are explored using free indirect discourse (FID) as a novel storytelling method to gain a multi-generational understanding of the experiences of…

Abstract

Informal conversational encounters are explored using free indirect discourse (FID) as a novel storytelling method to gain a multi-generational understanding of the experiences of women working in public relations (PR) in 1960s/1970s Britain.

Echoing a literary tradition, anonymised transcripts of recordings provide impressionist accounts that immerse the reader in the thoughts and feelings of novelistic characters. An informal network of women narrate their stories with a much younger listener enabling exploration of intergenerational relationships and the intersection of gender and age.

This unstructured approach develops a complex yet natural flow to create unique withness-understandings. The author/narrator introduces a conception of informal conversational encounters, supporting an organic approach of interweaving storying, everyday performance, situated accountings, narrative unfoldings and inside/outside points of view.

An interplay of multiple female voices reveals a degree of symmetry in fractal patterns of women's early career experiences over the duration of a generation. Facilitation of sense-making through intergenerational conversations connects with Mannheim's theory of generational unity.

Women's beginnings of PR careers in 1960s/1970s Britain demonstrate a liberal feminist perspective in taking responsibility for their careers and enjoyment beyond the workplace in a man's world.

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