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Found 14 papers, 12 papers with code
How COVID-19 has Impacted the Anti-Vaccine Discourse: A Large-Scale Twitter Study Spanning Pre-COVID and Post-COVID Era
The debate around vaccines has been going on for decades, but the COVID-19 pandemic showed how crucial it is to understand and mitigate anti-vaccine sentiments.
MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgments
Automatic summarization of legal case judgments is a practically important problem that has attracted substantial research efforts in many countries.
CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet Extraction
We then (pre)train an encoder-decoder model by applying contrastive learning on the decoder-generated aspect-aware sentiment representations of the masked terms.
Ranked #2 on
Aspect Sentiment Triplet Extraction
on ASTE-Data-V2
Aspect Sentiment Triplet Extraction
Aspect Term Extraction and Sentiment Classification
+2
ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts
Despite tremendous progress in automatic summarization, state-of-the-art methods are predominantly trained to excel in summarizing short newswire articles, or documents with strong layout biases such as scientific articles or government reports.
Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation
Summarization of legal case judgement documents is a challenging problem in Legal NLP.
ETMS@IITKGP at SemEval-2022 Task 10: Structured Sentiment Analysis Using A Generative Approach
Structured Sentiment Analysis (SSA) deals with extracting opinion tuples in a text, where each tuple (h, e, t, p) consists of h, the holder, who expresses a sentiment polarity p towards a target t through a sentiment expression e. While prior works explore graph-based or sequence labeling-based approaches for the task, we in this paper present a novel unified generative method to solve SSA, a SemEval2022 shared task.
CAVES: A Dataset to facilitate Explainable Classification and Summarization of Concerns towards COVID Vaccines
This is also the first multi-label classification dataset that provides explanations for each of the labels.
MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs
Occurrences of catastrophes such as natural or man-made disasters trigger the spread of rumours over social media at a rapid pace.
PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinion term/span explaining the rationale behind the sentiment.
Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach
For the regression task, VADEC, when trained with SenWave, achieves 7. 6% and 16. 5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions respectively.
Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild
With the exponential growth of online marketplaces and user-generated content therein, aspect-based sentiment analysis has become more important than ever.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
How Have We Reacted To The COVID-19 Pandemic? Analyzing Changing Indian Emotions Through The Lens of Twitter
Since its outbreak, the ongoing COVID-19 pandemic has caused unprecedented losses to human lives and economies around the world.
Read what you need: Controllable Aspect-based Opinion Summarization of Tourist Reviews
Manually extracting relevant aspects and opinions from large volumes of user-generated text is a time-consuming process.
StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures
A novel non-stationarity visualization tool known as StationPlot is developed for deciphering the chaotic behavior of a dynamical time series.
