Search Results for author:
Found 6 papers, 2 papers with code
Exploiting Representation Bias for Data Distillation in Abstractive Text Summarization
We employ clustering techniques to learn the diversity of a model's sample space and how data points are mapped from the embedding space to the encoder space and vice versa.
Fusing Multimodal Signals on Hyper-complex Space for Extreme Abstractive Text Summarization (TL;DR) of Scientific Contents
In this paper, we deal with a novel task of extreme abstractive text summarization (aka TL;DR generation) by leveraging multiple input modalities.
Inline Citation Classification using Peripheral Context and Time-evolving Augmentation
Current datasets and methods used for inline citation classification only use citation-marked sentences constraining the model to turn a blind eye to domain knowledge and neighboring contextual sentences.
Assessing the Quality of the Datasets by Identifying Mislabeled Samples
Therefore, by identifying the outliers in the latent space, we can find the mislabeled samples.
See, Hear, Read: Leveraging Multimodality with Guided Attention for Abstractive Text Summarization
However, existing methods use short videos as the visual modality and short summary as the ground-truth, therefore, perform poorly on lengthy videos and long ground-truth summary.
Corpora Evaluation and System Bias Detection in Multi-document Summarization
Owing to no standard definition of the task, we encounter a plethora of datasets with varying levels of overlap and conflict between participating documents.
