DSGPT: Domain-Specific Generative Pre-Training of Transformers for Text Generation in E-commerce Title and Review Summarization

Context processing plays an important role in different Natural Language Processing applications. Sentence ordering is one of critical tasks in text generation. Following the same order of sentences in the row sources of text is not necessarily to be applied for the resulted text. Accordingly, a need for chronological sentence ordering is of high importance in this regard. Some researches followed linguistic syntactic analysis and others used statistical approaches. This paper proposes a new model for sentence ordering based on sematic analysis. Word level semantics forms a seed to sentence level sematic relations. The model introduces a clustering technique based on sentences senses relatedness. Following to this, sentences are chronologically ordered through two main steps; overlap detection and chronological cause-effect rules. Overlap detection drills down into each cluster to step through its sentences in chronological sequence. Cause-effect rules forms the linguistic knowledge controlling sentences relations. Evaluation of the proposed algorithm showed the capability of the proposed model to process size free texts, non-domain specific and open to extend the cause-effect rules for specific ordering needs.

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Synthesizing Aspect-Driven Recommendation Explanations from Reviews

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/336 ◽

2020 ◽

Author(s):

Trung-Hoang Le ◽

Hady W. Lauw

Keyword(s):

Text Generation ◽

Model Experiments ◽

Product Categories ◽

Review Summarization ◽

Explainable Recommendation

Explanations help users make sense of recommendations, increasing the likelihood of adoption. Existing approaches to explainable recommendations tend to rely on rigidly standardized templates, only allowing fill-in-the-blank aspect-level sentiments. For more flexible, literate, and varied explanations that cover various aspects of interest, we propose to synthesize an explanation by selecting snippets from reviews to optimize representativeness and coherence. To fit the target user's aspect preferences, we contextualize the opinions based on a compatible explainable recommendation model. Experiments on datasets of varying product categories showcase the efficacies of our method as compared to baselines based on templates, review summarization, selection, and text generation.

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A Global Model for Concept-to-Text Generation

Journal of Artificial Intelligence Research ◽

10.1613/jair.4025 ◽

2013 ◽

Vol 48 ◽

pp. 305-346 ◽

Cited By ~ 12

Author(s):

I. Konstas ◽

M. Lapata

Keyword(s):

State Of The Art ◽

Generation Process ◽

Text Generation ◽

Additional Information ◽

Domain Specific ◽

Content Selection ◽

Linguistic Input ◽

Context Free ◽

Domain Independent ◽

Probabilistic Context

Concept-to-text generation refers to the task of automatically producing textual output from non-linguistic input. We present a joint model that captures content selection ("what to say") and surface realization ("how to say") in an unsupervised domain-independent fashion. Rather than breaking up the generation process into a sequence of local decisions, we define a probabilistic context-free grammar that globally describes the inherent structure of the input (a corpus of database records and text describing some of them). We recast generation as the task of finding the best derivation tree for a set of database records and describe an algorithm for decoding in this framework that allows to intersect the grammar with additional information capturing fluency and syntactic well-formedness constraints. Experimental evaluation on several domains achieves results competitive with state-of-the-art systems that use domain specific constraints, explicit feature engineering or labeled data.

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Scientific Problem Solving in a Virtual Laboratory: A Comparison Between Individuals and Pairs

Swiss Journal of Psychology ◽

10.1024/1421-0185.67.2.71 ◽

2008 ◽

Vol 67 (2) ◽

pp. 71-83 ◽

Cited By ~ 5

Author(s):

Yolanda A. Métrailler ◽

Ester Reijnen ◽

Cornelia Kneser ◽

Klaus Opwis

Keyword(s):

Visual Search ◽

Problem Solving ◽

Process Data ◽

Scientific Problem ◽

Domain Specific ◽

Scientific Problem Solving ◽

Before And After ◽

Verbal Data ◽

Psychological Knowledge ◽

Problem Solving Task

This study compared individuals with pairs in a scientific problem-solving task. Participants interacted with a virtual psychological laboratory called Virtue to reason about a visual search theory. To this end, they created hypotheses, designed experiments, and analyzed and interpreted the results of their experiments in order to discover which of five possible factors affected the visual search process. Before and after their interaction with Virtue, participants took a test measuring theoretical and methodological knowledge. In addition, process data reflecting participants’ experimental activities and verbal data were collected. The results showed a significant but equal increase in knowledge for both groups. We found differences between individuals and pairs in the evaluation of hypotheses in the process data, and in descriptive and explanatory statements in the verbal data. Interacting with Virtue helped all students improve their domain-specific and domain-general psychological knowledge.

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Emotions and health: The impact of emotions, emotions regulation, music, and acculturation on health

Zeitschrift für Gesundheitspsychologie ◽

10.1026/0943-8149.16.3.112 ◽

2008 ◽

Vol 16 (3) ◽

pp. 112-115 ◽

Cited By ~ 1

Author(s):

Stephan Bongard ◽

Volker Hodapp ◽

Sonja Rohrmann

Keyword(s):

Stress Responses ◽

Physiological Stress ◽

Anger Expression ◽

Domain Specific ◽

Cardiovascular Stress ◽

Expression Behavior ◽

Cardio Vascular ◽

And Coping ◽

Relationship Of ◽

The Impact

Abstract. Our unit investigates the relationship of emotional processes (experience, expression, and coping), their physiological correlates and possible health outcomes. We study domain specific anger expression behavior and associated cardio-vascular loads and found e.g. that particularly an open anger expression at work is associated with greater blood pressure. Furthermore, we demonstrated that women may be predisposed for the development of certain mental disorders because of their higher disgust sensitivity. We also pointed out that the suppression of negative emotions leads to increased physiological stress responses which results in a higher risk for cardiovascular diseases. We could show that relaxation as well as music activity like singing in a choir causes increases in the local immune parameter immunoglobuline A. Finally, we are investigating connections between migrants’ strategy of acculturation and health and found e.g. elevated cardiovascular stress responses in migrants when they where highly adapted to the German culture.

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Measurement Invariance of Divergent Thinking Across Gender, Age, and School Forms

European Journal of Psychological Assessment ◽

10.1027/1015-5759.25.1.1 ◽

2009 ◽

Vol 25 (1) ◽

pp. 1-7 ◽

Cited By ~ 12

Author(s):

Jörg-Tobias Kuhn ◽

Heinz Holling

Keyword(s):

Measurement Invariance ◽

Divergent Thinking ◽

Factor Model ◽

Age Groups ◽

Thinking Skills ◽

Specific Model ◽

Confirmatory Factor Analyses ◽

Domain Specific ◽

Gender And Age ◽

Confirmatory Factor

The present study explores the factorial structure and the degree of measurement invariance of 12 divergent thinking tests. In a large sample of German students (N = 1328), a three-factor model representing verbal, figural, and numerical divergent thinking was supported. Multigroup confirmatory factor analyses revealed that partial strong measurement invariance was tenable across gender and age groups as well as school forms. Latent mean comparisons resulted in significantly higher divergent thinking skills for females and students in schools with higher mean IQ. Older students exhibited higher latent means on the verbal and figural factor, but not on the numerical factor. These results suggest that a domain-specific model of divergent thinking may be assumed, although further research is needed to elucidate the sources that negatively affect measurement invariance.

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