On‐demand recent personal tweets summarization on mobile devices

Chin, Jin Yao; Bhowmick, Sourav S.; Jatowt, Adam

doi:10.1002/asi.24137

Cited by 6 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The summaries generated are based on a query‐biased concept concerning the information extracted from tweets. Another work that uses information from Twitter is Chin, Bhowmick, and Jatowt (2019). Here, the authors use Latent Dirichlet Allocation (LDA) Blei, Ng, and Jordan (2003) topic modeling to assign similar tweets to the same topic and then, generate a ranking of relevant tweets to create a summary for each topic.…”

Section: Related Workmentioning

confidence: 99%

Authority and priority signals in automatic summary generation for online reputation management

Rodríguez‐Vidal

Carrillo-de-Albornoz

Gonzalo

et al. 2020

Asso for Info Science & Tech

View full text Add to dashboard Cite

Online reputation management (ORM) comprises the collection of techniques that help monitoring and improving the public image of an entity (companies, products, institutions) on the Internet. The ORM experts try to minimize the negative impact of the information about an entity while maximizing the positive material for being more trustworthy to the customers. Due to the huge amount of information that is published on the Internet every day, there is a need to summarize the entire flow of information to obtain only those data that are relevant to the entities. Traditionally the automatic summarization task in the ORM scenario takes some in‐domain signals into account such as popularity, polarity for reputation and novelty but exists other feature to be considered, the authority of the people. This authority depends on the ability to convince others and therefore to influence opinions. In this work, we propose the use of authority signals that measures the influence of a user jointly with (a) priority signals related to the ORM domain and (b) information regarding the different topics that influential people is talking about. Our results indicate that the use of authority signals may significantly improve the quality of the summaries that are automatically generated.

show abstract

Section: Related Workmentioning

confidence: 99%

Authority and priority signals in automatic summary generation for online reputation management

Rodríguez‐Vidal

Carrillo-de-Albornoz

Gonzalo

et al. 2020

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

“…Naik et al (2018) proposed a summarization system for tweets that makes use of the Particle Swarm Optimization (PSO) algorithm (Kennedy et al (1997)). Chin et al (2019) designed tweets summarization engine for mobile devices based on Latent Dirichlet Allocation (LDA) topic modeling.…”

Section: Introductionmentioning

confidence: 99%

Semrank

Singh

2021

International Journal of Cognitive Informatics and Natural Intelligence

View full text Add to dashboard Cite

Popular real-world events often create huge traffic on Twitter including real-time updates of important moments, personal comments, and so on while the event is happening. Most of the users are interested to read the important tweets that possibly include important moments of that event. However, extracting the relevant tweets of any event is a challenging task due to the endless stream of noisy tweets and vocabulary variation problem of social media content. To handle these challenges, the authors introduce a new approach for computing the relative tweet importance based on the concept of the Pagerank algorithm where adjacency matrix of the graph representation of tweets contains semantic similarity matrix based on the word mover's distance measure utilizing Word2Vec word embedding model. The results show that top-ranked tweets generated by the proposed approach are more concise and news-worthy than baseline approaches.

show abstract

“…Jung and Lee, 2019), text summarisation (e.g. Chin et al ., 2019; Huang et al ., 2018) and automated essay scoring (e.g. Chen et al ., 2016; Kakkonen et al ., 2006).…”

Section: Introductionmentioning

confidence: 99%

Examining research topics with a dependency-based noun phrase extraction method: a case in accounting

Lei

Deng

Liu

2020

LHT

View full text Add to dashboard Cite

PurposeExamining research topics in a specific area such as accounting is important to both novice and veteran researchers. The present study aims to identify the research topics in the area of accounting and to investigate the research trends by finding hot and cold topics from all those identified ones in the field.Design/methodology/approachA new dependency-based method focusing on noun phrases, which efficiently extracts research topics from a large set of library data, was proposed. An AR(1) autoregressive model was used to identify topics that have received significantly more or less attention from the researchers. The data used in the study included a total of 4,182 abstracts published in six leading (or premier) accounting journals from 2000 to May 2019.FindingsThe study identified 48 important research topics across the examined period as well as eight hot topics and one cold topic from the 48 topics.Originality/valueThe research topics identified based on the dependency-based method are similar to those found with the technique of latent Dirichlet allocation latent Dirichlet allocation (LDA) topic modelling. In addition, the method seems highly efficient, and the results are easier to interpret. Last, the research topics and trends found in the study provide reference to the researchers in the area of accounting.

show abstract

On‐demand recent personal tweets summarization on mobile devices

Cited by 6 publications

References 22 publications

Authority and priority signals in automatic summary generation for online reputation management

Authority and priority signals in automatic summary generation for online reputation management

Semrank

Examining research topics with a dependency-based noun phrase extraction method: a case in accounting

Contact Info

Product

Resources

About