Search Results for author:
Found 6 papers, 1 papers with code
ADSumm: Annotated Ground-truth Summary Datasets for Disaster Tweet Summarization
In the literature, there are two types of approaches for disaster summarization, namely, supervised and unsupervised approaches.
ATSumm: Auxiliary information enhanced approach for abstractive disaster Tweet Summarization with sparse training data
The initial phase, known as the extractive phase, involves identifying the most relevant tweets.
TONE: A 3-Tiered ONtology for Emotion analysis
Furthermore, we describe three distinct use cases that demonstrate the applicability of our ontology.
IKDSumm: Incorporating Key-phrases into BERT for extractive Disaster Tweet Summarization
Therefore, given tweets related to a disaster, IKDSumm ensures fulfillment of the summarization key objectives, such as information coverage, relevance, and diversity in summary without any human intervention.
PORTRAIT: a hybrid aPproach tO cReate extractive ground-TRuth summAry for dIsaster evenT
We validate the effectiveness of PORTRAIT on 5 disaster events through quantitative and qualitative comparisons of ground-truth summaries generated by existing intuitive approaches, a semi-automated approach, and PORTRAIT.
Predicting Tomorrow's Headline using Today's Twitter Deliberations
In our proposed model, we have considered the users' involvement as well as the users' reaction towards an article to predict the popularity of the article.
