Automatic Text Summarization by Providing Coverage, Non-Redundancy, and Novelty Using Sentence Graph

The day-to-day growth of online information necessitates intensive research in automatic text summarization (ATS). The ATS software produces summary text by extracting important information from the original text. With the help of summaries, users can easily read and understand the documents of interest. Most of the approaches for ATS used only local properties of text. Moreover, the numerous properties make the sentence selection difficult and complicated. So this article uses a graph based summarization to utilize structural and global properties of text. It introduces maximal clique based sentence selection (MCBSS) algorithm to select important and non-redundant sentences that cover all concepts of the input text for summary. The MCBSS algorithm finds novel information using maximal cliques (MCs). The experimental results of recall oriented understudy for gisting evaluation (ROUGE) on Timeline dataset show that the proposed work outperforms the existing graph algorithms Bushy Path (BP), Aggregate Similarity (AS), and TextRank (TR).

A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot

In recent times, a large number of people have been involved in establishing their own businesses. Unlike humans, chatbots can serve multiple customers at a time, are available 24/7 and reply in less than a fraction of a second. Though chatbots perform well in task-oriented activities, in most cases they fail to understand personalized opinions, statements or even queries which later impact the organization for poor service management. Lack of understanding capabilities in bots disinterest humans to continue conversations with them. Usually, chatbots give absurd responses when they are unable to interpret a user’s text accurately. Extracting the client reviews from conversations by using chatbots, organizations can reduce the major gap of understanding between the users and the chatbot and improve their quality of products and services.Thus, in our research we incorporated all the key elements that are necessary for a chatbot to analyse andunderstand an input text precisely and accurately. We performed sentiment analysis, emotion detection, intent classification and named-entity recognition using deep learning to develop chatbots with humanistic understanding and intelligence. The efficiency of our approach can be demonstrated accordingly by the detailed analysis.

Soft Rough Set based span for unsupervised keyword extraction

The present work proposes an application of Soft Rough Set and its span for unsupervised keyword extraction. In recent times Soft Rough Sets are being applied in various domains, though none of its applications are in the area of keyword extraction. On the other hand, the concept of Rough Set based span has been developed for improved efficiency in the domain of extractive text summarization. In this work we amalgamate these two techniques, called Soft Rough Set based Span (SRS), to provide an effective solution for keyword extraction from texts. The universe for Soft Rough Set is taken to be a collection of words from the input texts. SRS provides an ideal platform for identifying the set of keywords from the input text which cannot always be defined clearly and unambiguously. The proposed technique uses greedy algorithm for computing spanning sets. The experimental results suggest that extraction of keywords using the proposed scheme gives consistent results across different domains. Also, it has been found to be more efficient in comparison with several existing unsupervised techniques.

Terminal++ for Robot Researcher Using C#

Purpose: Sometimes our robot researcher needs a terminal program to exchange the data with the robot or automation device. Nevertheless, the readily available terminal program lacks some functionality that is most relevant to the researcher. We feel that a featured rich terminal program can handle lots of communication overhead for the researcher and relieve them from repetitive and time-consuming tasks. In mind for this, we researched and developed a utility program. We added extra features like automatic send, change dynamic data, etc., so our robot researcher can test the system communication better. In this paper, we demonstrated the utility program in detail. It is built using C#, which is under the Microsoft dot net framework. The code is uploaded to GitHub. Anyone can download and use it. It can be customized for their need. All used classes are available in .cs format. Design/Methodology/Approach: This is the software utility program built by the dot net framework of Microsoft visual studio. It has a graphical user interface (GUI) and some object classes. It has a serial and ethernet interface to test the channel. Once the medium is selected, the application will send whatever is written in the input text box. The Data sending may be an automatic or manual process. In manual mode, after typing the command, we need to press the “Enter” key to send the data. In automatic mode, it will send automatically within the preset interval. The transmit and receive content is displayed inside the list box. Findings/results: sometimes, our project goes into a critical phase. We need to have good tools to overcome the situation immediately. This is a helpful tool to trace the communication-related issue. Using this tool, we can observe the outgoing and incoming data traffic. The robot researcher can use it for their communication-related debug purposes. Originality/Value: Using this terminal program, our robot researcher will get lots of benefits where readily available utility programs cannot provide them. It has some unique features like automatic sending, changing dynamic content, etc. It has a serial and ethernet interface channel so that most of the device communication can be debugged through this interface software. It is entirely free and open source. Anyone can download and use it for personal as well as commercial purposes. Paper Type: Experiment-based Research.

TIPS: A Framework for Text Summarising with Illustrative Pictures

We propose an algorithm to generate graphical summarising of longer text passages using a set of illustrative pictures (TIPS). TIPS is an algorithm using a voting process that uses results of individual “weak” algorithms. The proposed method includes a summarising algorithm that generates a digest of the input document. Each sentence of the text summary is used as the input for further processing by the sentence transformer separately. A sentence transformer performs text embedding and a group of CLIP similarity-based algorithms trained on different image embedding finds semantic distances between images in the illustration image database and the input text. A voting process extracts the most matching images to the text. The TIPS algorithm allows the integration of the best (highest scored) results of the different recommendation algorithms by diminishing the influence of images that are a disjointed part of the recommendations of the component algorithms. TIPS returns a set of illustrative images that describe each sentence of the text summary. Three human judges found that the use of TIPS resulted in an increase in matching highly relevant images to text, ranging from 5% to 8% and images relevant to text ranging from 3% to 7% compared to the approach based on single-embedding schema.

Investigating the effects of corpus and configuration on assistive input methods

<p>Assistive technologies aim to provide assistance to those who are unable to perform various tasks in their day-to-day lives without tremendous difficulty. This includes — amongst other things — communicating with others. Augmentative and adaptive communication (AAC) is a branch of assistive technologies which aims to make communicating easier for people with disabilities which would otherwise prevent them from communicating efficiently (or, in some cases, at all). The input rate of these communication aids, however, is often constrained by the limited number of inputs found on the devices and the speed at which the user can toggle these inputs. A similar restriction is also often found on smaller devices such as mobile phones: these devices also often require the user to input text with a smaller input set, which often results in slower typing speeds. Several technologies exist with the purpose of improving the text input rates of these devices. These technologies include ambiguous keyboards, which allow users to input text using a single keypress for each character and trying to predict the desired word; word prediction systems, which attempt to predict the word the user is attempting to input before he or she has completed it; and word auto-completion systems, which complete the entry of predicted words before all the corresponding inputs have been pressed. This thesis discusses the design and implementation of a system incorporating the three aforementioned assistive input methods, and presents several questions regarding the nature of these technologies. The designed system is found to outperform a standard computer keyboard in many situations, which is a vast improvement over many other AAC technologies. A set of experiments was designed and performed to answer the proposed questions, and the results of the experiments determine that the corpus used to train the system — along with other tuning parameters — have a great impact on the performance of the system. Finally, the thesis also discusses the impact that corpus size has on the memory usage and response time of the system.</p>

Investigating the effects of corpus and configuration on assistive input methods

<p>Assistive technologies aim to provide assistance to those who are unable to perform various tasks in their day-to-day lives without tremendous difficulty. This includes — amongst other things — communicating with others. Augmentative and adaptive communication (AAC) is a branch of assistive technologies which aims to make communicating easier for people with disabilities which would otherwise prevent them from communicating efficiently (or, in some cases, at all). The input rate of these communication aids, however, is often constrained by the limited number of inputs found on the devices and the speed at which the user can toggle these inputs. A similar restriction is also often found on smaller devices such as mobile phones: these devices also often require the user to input text with a smaller input set, which often results in slower typing speeds. Several technologies exist with the purpose of improving the text input rates of these devices. These technologies include ambiguous keyboards, which allow users to input text using a single keypress for each character and trying to predict the desired word; word prediction systems, which attempt to predict the word the user is attempting to input before he or she has completed it; and word auto-completion systems, which complete the entry of predicted words before all the corresponding inputs have been pressed. This thesis discusses the design and implementation of a system incorporating the three aforementioned assistive input methods, and presents several questions regarding the nature of these technologies. The designed system is found to outperform a standard computer keyboard in many situations, which is a vast improvement over many other AAC technologies. A set of experiments was designed and performed to answer the proposed questions, and the results of the experiments determine that the corpus used to train the system — along with other tuning parameters — have a great impact on the performance of the system. Finally, the thesis also discusses the impact that corpus size has on the memory usage and response time of the system.</p>

The Patch: An Artful Syn(aes)thetic Mapping of Linguistic Data through Collaborative Digital / Analogue Literacy Processes

The Patch workshop explores creative / critical analyses that can map the collectively relevant topoi of semiosis in linguistic texts according to the three ecologies as articulated by Félix Guattari. As creative pedagogues both in service and critical of creative economics, we valourize a generative practice, one that results in successive creative readings, writings, visualizations, sonifications and audiovisual artifacts. The Patch is a human-computer procedural algorithm, engaging a series of recursive and recombinant processes that utilize several software programs, collaborative writing and performance practices to bridge analogue and digital literacies. A total of 80 teacher education students, graduate students and faculty, working with a single input text, provided the data reported in this paper.

Design of Chinese-English Wireless Simultaneous Interpretation System Based on Speech Recognition Technology

A Chinese-English wireless simultaneous interpretation system based on speech recognition technology is suggested to solve the problems of low translation accuracy and a high number of ambiguous terms in current Chinese-English simultaneous interpretation systems. The system’s general structure and hardware architecture are summarized. The chairman unit, representative unit, transliteration unit, and auditing unit are the four basic components of the simultaneous interpretation system. The CPU is the nRF24E1 hardware wireless radio frequency transceiver chip, while the chairman machine, representative machine, translator, and auditorium are all created separately. Speech recognition technology is used by the system software to create a speech recognition process that properly produces speech-related semantics. The input text is used as the search criteria, a manual interactive synchronous translation program is created, and the results for the optimum translation impact are trimmed. The experimental findings reveal that this system’s sentence translation accuracy rate is 0.9–1.0, and the number of ambiguous terms is minimal, which is an improvement on previous systems’ low translation accuracy.

VNLP: Visible natural language processing

In general, Natural Language Processing (NLP) algorithms exhibit black-box behavior. Users input text and output are provided with no explanation of how the results are obtained. In order to increase understanding and trust, users value transparent processing which may explain derived results and enable understanding of the underlying routines. Many approaches take an opaque approach by default when designing NLP tools and do not incorporate a means to steer and manipulate the intermediate NLP steps. We present an interactive, customizable, visual framework that enables users to observe and participate in the NLP pipeline processes, explicitly manipulate the parameters of each step, and explore the result visually based on user preferences. The visible NLP (VNLP) pipeline design is then applied to a text similarity application to demonstrate the utility and advantages of a visible and transparent NLP pipeline in supporting users to understand and justify both the process and results. We also report feedback on our framework from a modern languages expert.