Improving Document Summarization by Incorporating Social Contextual Information

2011 ◽  
pp. 499-508 ◽  
Author(s):  
Po Hu ◽  
Donghong Ji ◽  
Cheng Sun ◽  
Chong Teng ◽  
Yong Zhang
Keyword(s):  
Contextual Information ◽  
Document Summarization

A Medieval Burial from Graham Street, Leith, Edinburgh

2003 ◽  
Vol 25 (2) ◽  
pp. 165-169
Author(s):  
Paul R. J. Duffy ◽  
Olivia Lelong
Keyword(s):  
Contextual Information ◽  
Human Remains ◽  
Housing Development ◽  
Archaeological Research ◽  
Archaeological Excavation ◽  
Radiocarbon Dates ◽  
Physical Evidence ◽  
Research Division ◽  
The North ◽  
The Individual

Summary An archaeological excavation was carried out at Graham Street, Leith, Edinburgh by Glasgow University Archaeological Research Division (GUARD) as part of the Historic Scotland Human Remains Call-off Contract following the discovery of human remains during machine excavation of a foundation trench for a new housing development. Excavation demonstrated that the burial was that of a young adult male who had been interred in a supine position with his head orientated towards the north. Radiocarbon dates obtained from a right tibia suggest the individual died between the 15th and 17th centuries AD. Little contextual information exists in documentary or cartographic sources to supplement this scant physical evidence. Accordingly, it is difficult to further refine the context of burial, although a possible link with a historically attested siege or a plague cannot be discounted.

PRIMING AND THE PREDICTABILITY OF CONTEXTUAL INFORMATION

2004 ◽  
Vol 99 (7) ◽  
pp. 1075
Author(s):  
MAURA PILOTTI
Keyword(s):  
Contextual Information

Multi-document summarization using distortion-rate ratio

2014 ◽  
Author(s):  
Ulukbek Attokurov ◽  
Ulug Bayazit
Keyword(s):  
Rate Ratio ◽  
Document Summarization ◽  
Distortion Rate

"He seems odd": The effects of risk-irrelevant information and actuarial risk estimates on mock jurors' perceptions of sexual recidivism risk

2020 ◽  
Author(s):  
Jennifer Kamorowski ◽  
Karl Ask ◽  
Maartje Schreuder ◽  
Marko Jelicic ◽  
Corine de Ruiter
Keyword(s):  
Contextual Information ◽  
Individual Characteristics ◽  
Risk Estimate ◽  
Case Vignette ◽  
Future Research ◽  
Sexual Recidivism ◽  
Mock Jurors ◽  
Related Factors ◽  
Risk Estimates ◽  
Actuarial Risk

Previous research has shown that mock and actual jurors give little weight to actuarial sexual offending recidivism risk estimates when making decisions regarding civil commitment for so-called sexually violent predators (SVPs). We hypothesized that non-risk related factors, such as irrelevant contextual information and jurors’ information-processing style, would influence mock jurors’ perceptions of sexual recidivism risk. This preregistered experimental study examined the effects of mock jurors’ (N = 427) need for cognition (NFC), irrelevant contextual information in the form of the offender’s social attractiveness, and an actuarial risk estimate on mock jurors’ estimates of sexual recidivism risk related to a simulated SVP case vignette. Mock jurors exposed to negative risk-irrelevant characteristics of the offender estimated sexual recidivism risk as higher than mock jurors exposed to positive information about the offender. However, this effect was no longer significant after mock jurors had reviewed Static-99R actuarial risk estimate information. We found no support for the hypothesis that the level of NFC moderates the relationship between risk-irrelevant contextual information and risk estimates. Future research could explore additional individual characteristics or attitudes among mock jurors that may influence perceptions of sexual recidivism risk and insensitivity to actuarial risk estimates.

Minimizing Bias in Forensic Handwriting Examinations

2015 ◽  
Vol 25 ◽  
pp. 17-26 ◽  
Author(s):  
L. C. Alewijnse ◽  
E.J.A.T. Mattijssen ◽  
R.D. Stoel
Keyword(s):  
Decision Making ◽  
Information Management ◽  
Contextual Information ◽  
Context Information ◽  
Decision Making Process ◽  
Potential Bias ◽  
Potential Influence ◽  
Management Procedures

The purpose of this paper is to contribute to the increasing awareness about the potential bias on the interpretation and conclusions of forensic handwriting examiners (FHEs) by contextual information. We briefly provide the reader with an overview of relevant types of bias, the difficulties associated with studying bias, the sources of bias and their potential influence on the decision making process in casework, and solutions to minimize bias in casework. We propose that the limitations of published studies on bias need to be recognized and that their conclusions must be interpreted with care. Instead of discussing whether bias is an issue in casework, the forensic handwriting community should actually focus on how bias can be minimized in practice. As some authors have already shown (e.g., Found & Ganas, 2014), it is relatively easy to implement context information management procedures in practice. By introducing appropriate procedures to minimize bias, not only forensic handwriting examination will be improved, it will also increase the acceptability of the provided evidence during court hearings. Purchase Article - $10

Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography

2020 ◽  
Vol 16 (8) ◽  
pp. 1004-1021
Author(s):  
Xiaoqi Lu ◽  
Yu Gu ◽  
Lidong Yang ◽  
Baohua Zhang ◽  
Ying Zhao ◽  
...  
Keyword(s):  
False Positive ◽  
Contextual Information ◽  
Lung Nodule ◽  
Positive Sample ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Level Information ◽  
Multi Level ◽  
Cade System

Objective: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. Methods: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. Results: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. Conclusion: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

An Automatic Text Summarization Method with the Concern of Covering Complete Formation

2020 ◽  
Vol 13 (5) ◽  
pp. 977-986
Author(s):  
Srinivasa Rao Kongara ◽  
Dasika Sree Rama Chandra Murthy ◽  
Gangadhara Rao Kancherla
Keyword(s):  
Research Method ◽  
Research Work ◽  
Fuzzy Rule ◽  
Text Summarization ◽  
Document Summarization ◽  
Summarization Method ◽  
Overall Evaluation ◽  
Multiple Documents ◽  
Rule System ◽  
Value Decomposition

Background: Text summarization is the process of generating a short description of the entire document which is more difficult to read. This method provides a convenient way of extracting the most useful information and a short summary of the documents. In the existing research work, this is focused by introducing the Fuzzy Rule-based Automated Summarization Method (FRASM). Existing work tends to have various limitations which might limit its applicability to the various real-world applications. The existing method is only suitable for the single document summarization where various applications such as research industries tend to summarize information from multiple documents. Methods: This paper proposed Multi-document Automated Summarization Method (MDASM) to introduce the summarization framework which would result in the accurate summarized outcome from the multiple documents. In this work, multi-document summarization is performed whereas in the existing system only single document summarization was performed. Initially document clustering is performed using modified k means cluster algorithm to group the similar kind of documents that provides the same meaning. This is identified by measuring the frequent term measurement. After clustering, pre-processing is performed by introducing the Hybrid TF-IDF and Singular value decomposition technique which would eliminate the irrelevant content and would result in the required content. Then sentence measurement is one by introducing the additional metrics namely Title measurement in addition to the existing work metrics to accurately retrieve the sentences with more similarity. Finally, a fuzzy rule system is applied to perform text summarization. Results: The overall evaluation of the research work is conducted in the MatLab simulation environment from which it is proved that the proposed research method ensures the optimal outcome than the existing research method in terms of accurate summarization. MDASM produces 89.28% increased accuracy, 89.28% increased precision, 89.36% increased recall value and 70% increased the f-measure value which performs better than FRASM. Conclusion: The summarization processes carried out in this work provides the accurate summarized outcome.

Sign in / Sign up

Export Citation Format

Share Document