An HCI Approach to Extractive Text Summarization: Selecting Key Sentences Based on User Copy Operations

Kirsh, Ilan; Joy, Mike

doi:10.1007/978-3-030-60700-5_43

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Complex words can be simplified or a metadata attached could be the action for better understanding of readers [14]. On the contrary frequently copied sentences can be a positive key performance indicator of a valid content and can be further utilized for functionality like -automatic text summarization [3], recommendations, etc. This data can be https:// journal.uob.edu.bh used to understand the users interest and also for Search engine optimization [4], [13].…”

Section: B Implicit Feedback Methods In Desktop Systemsmentioning

confidence: 99%

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Machine Learning Based Smartphone Screen GestureRecognition Using Smartphone Embedded Accelerometer and Gyroscope

Bhatele,

Bedekar

2024

IJCDS

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In the recent times, smartphone usage has become increasingly popular for learning. User's exhibit multiple gesture interactions with smartphones, while reading, which can provide valuable implicit feedback about the content consumed. Smartphones have many embedded sensors which capture plethora of user interaction data. The on-device Gyroscope and Accelerometer can be enabled to capture the variations done due to gesture interactions like scrolling, pinch to zoom, tap, orientation change and screen capture. This research work is based on training machine learning classifier models with smartphone sensors' readings to identify the users screen gesture interactions. 47 users performed various screen gestures using an android application and contributed in data collection activity. Aggregated time domain feature extraction has been computed on the preprocessed data. Four groups of data have been used to train the models. Extensive experiments are done to test the success of proposed system using Random Forests (RFC), Support Vector Machine (SVM), Extreme Gradient Boost (XGB), Adaptive Boost (ADA Boost), Naïve Bayes (NB) and K-Nearest Neighbour (KNN). Detailed analysis of the success rate and accuracy calculation have been performed. Best identification accuracy of 97.58% is achieved by Random Forest Classifier followed by Extreme Gradient Boost (XGB) and K-Nearest Neighbour (KNN) with accuracy 95.97% and 93.55% respectively.

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Section: B Implicit Feedback Methods In Desktop Systemsmentioning

confidence: 99%

“…Data frequently copied can also has been studied to be positive for phrases, codes and paragraphs. On the contrary it has been demonstrated a negative indicator for words [3], [4], [5].…”

Section: Introductionmentioning

confidence: 93%

Machine Learning Based Smartphone Screen GestureRecognition Using Smartphone Embedded Accelerometer and Gyroscope

Bhatele,

Bedekar

2024

IJCDS

View full text Add to dashboard Cite

show abstract

“…Data frequently copied can also has been studied to be positive for phrases, codes and paragraphs. On the contrary it has been demonstrated a negative indicator for words [2,3,8].…”

Section: Introductionmentioning

confidence: 93%

“…Complex words can be simplified or a metadata attached could be the action for better understanding of readers [25]. On the contrary frequently copied sentences can be a positive key performance indicator of a valid content and can be further utilized for functionality like -automatic text summarization [2], recommendations, etc. This data can be used to understand the users interest and also for Search engine optimization [3,24].…”

Section: A Smartphone As a Pedagogical Toolmentioning

confidence: 99%

Machine Learning Based Smartphone Screen Gesture Recognition Using Smartphone Embedded Accelerometer and Gyroscope

Bhatele,

Bedekar

2024

Preprint

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In the recent times, smartphone usage has become increasingly popular for learning. User’s exhibit multiple gesture interactions with smartphones, while reading, which can provide valuable implicit feedback about the content consumed. Smartphones have many embedded sensors which capture plethora of user interaction data. The on-device Gyroscope and Accelerometer can be enabled to capture the variations done due to gesture interactions like scrolling, pinch to zoom, tap, orientation change and screen capture. This research work is based on training machine learning classifier models with smartphone sensors’ readings to identify the users screen gesture interactions. Data for the classifier is collected by from 47 users in total using an android application. Aggregated time domain feature extraction has been computed on the preprocessed data. Four groups of data have been used to train the models. Extensive experiments are done to test the success of proposed system using Random Forests, Support Vector Machine (SVM), Extreme Gradient Boost (XGB), ADA boost, Naïve Bayes (NB) and K-Nearest Neighbour (KNN). Detailed analysis of the success rate and accuracy calculation have been performed. Best identification accuracy of 97.58% is achieved by Random Forest Classifier followed by Extreme Gradient Boost and K-Nearest Neighbour with accuracy 95.97% and 93.55% respectively.

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Automatic Complex Word Identification Using Implicit Feedback from User Copy Operations

Kirsh

2020

Web Information Systems Engineering – WISE 2020

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An HCI Approach to Extractive Text Summarization: Selecting Key Sentences Based on User Copy Operations

Cited by 7 publications

References 9 publications

Machine Learning Based Smartphone Screen GestureRecognition Using Smartphone Embedded Accelerometer and Gyroscope

Machine Learning Based Smartphone Screen GestureRecognition Using Smartphone Embedded Accelerometer and Gyroscope

Machine Learning Based Smartphone Screen Gesture Recognition Using Smartphone Embedded Accelerometer and Gyroscope

Automatic Complex Word Identification Using Implicit Feedback from User Copy Operations

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