Code summarization aims to generate concise natural language descriptions of source code, which can help improve program comprehension and maintenance. Recent studies show that syntactic and structural information extracted from abstract syntax trees
(ASTs) is conducive to summary generation. However, existing approaches fail to fully capture the rich information in ASTs because of the large size/depth of ASTs. In this paper, we propose a novel model CAST that hierarchically splits and reconstructs ASTs. First, we hierarchically split a large AST into a set of subtrees and utilize a recursive neural network to encode the subtrees. Then, we aggregate the embeddings of subtrees by reconstructing the split ASTs to get the representation of the complete AST. Finally, AST representation, together with source code embedding obtained by a vanilla code token encoder, is used for code summarization. Extensive experiments, including the ablation study and the human evaluation, on benchmarks have demonstrated the power of CAST. To facilitate reproducibility, our code and data are available at https://github.com/DeepSoftwareAnalytics/CAST.
Sentence splitting involves the segmentation of a sentence into two or more shorter sentences. It is a key component of sentence simplification, has been shown to help human comprehension and is a useful preprocessing step for NLP tasks such as summa
risation and relation extraction. While several methods and datasets have been proposed for developing sentence splitting models, little attention has been paid to how sentence splitting interacts with discourse structure. In this work, we focus on cases where the input text contains a discourse connective, which we refer to as discourse-based sentence splitting. We create synthetic and organic datasets for discourse-based splitting and explore different ways of combining these datasets using different model architectures. We show that pipeline models which use discourse structure to mediate sentence splitting outperform end-to-end models in learning the various ways of expressing a discourse relation but generate text that is less grammatical; that large scale synthetic data provides a better basis for learning than smaller scale organic data; and that training on discourse-focused, rather than on general sentence splitting data provides a better basis for discourse splitting.
Business Process Management (BPM) is the discipline which is responsible for management of discovering, analyzing, redesigning, monitoring, and controlling business processes. One of the most crucial tasks of BPM is discovering and modelling business
processes from text documents. In this paper, we present our system that resolves an end-to-end problem consisting of 1) recognizing conditional sentences from technical documents, 2) finding boundaries to extract conditional and resultant clauses from each conditional sentence, and 3) categorizing resultant clause as Action or Consequence which later helps to generate new steps in our business process model automatically. We created a new dataset and three models to solve this problem. Our best model achieved very promising results of 83.82, 87.84, and 85.75 for Precision, Recall, and F1, respectively, for extracting Condition, Action, and Consequence clauses using Exact Match metric.
Our present study aimed at evaluating vertical resorption
following ramus and chin grafts applied in the split space after sixmonth
implant delay.
This prospective clinical trial aims to investigate the change of
alveolar ridge width after immediate implantation using tow
deferent techniques in expanding the alveolar ridge. In this
study, 12 patients (9 female,3 male) with narrow alveolar ri
dges
in the maxilla were treated. 20 dental implants were
immediately installed after the expansion procedures of the
selected narrow ridges , 10 implants with Bone Spreader
technique , and 10 implants with Bone Splitting technique .
In this research the nuclear splitting level allowed energies are found for N+ in the NH3SO3 compound (sulfamic acid) when the angle between molecular axis and the axis of the applied magnetic field in the range. We noticed the splitting level by act
ing the strong magnetic field. Therefore, we calculated the contribution of electric quadruple in the splitting process and its average value was E0=-0.1987neV. Moreover, the splitting level energies were calculated at minimum and maximum spin values. In addition, we found the frequency difference Δω at each value of β-angle.
Existing bone loss still remains an important challenge when implant placement
is required to rehabilitate the compromised site. A new way to place dental implants in cases of alveolar
atrophy, described in some articles, is the ridge-splitting tec
hnique, which allows the ridge to be widened
by a less invasive procedure than the traditional grafting approaches. The aim of this study is to evaluate
the efficiency of this technique in bone augmentation, evaluate the relationship between peri-implant
alveolar bone loss after Use of the Bone Splitting Technique, and to study the alveolar ridge width before
and after split.
