Biomedical semantic text summarizer.

Background: Text summarization is a challenging problem in Natural Language Processing, which involves condensing the content of textual documents without losing their overall meaning and information content, In the domain of bio-medical research, summaries are critical for efficient data analysis and information retrieval. While several bio-medical text summarizers exist in the literature, they often miss out on an essential text aspect: text semantics.

Results: This paper proposes a novel extractive summarizer that preserves text semantics by utilizing bio-semantic models.

Hard- and soft-tissue augmentation around dental implant using ridge split and connective tissue graft for esthetic rehabilitation of atrophic anterior maxilla.

Esthetic rehabilitation of edentulous site with horizontal ridge deficiency presents as one of the most challenging situations in implant practice. With the increase in popularity of dental implants, augmentation of the alveolar ridge and soft tissue has also become a routine procedure in most of the cases. Although hard-tissue augmentation is performed by other specialists also, a periodontist needs to master the skills of soft-tissue management to deliver esthetic and functional results in implant-supported prostheses.