Search Results for author:
Found 16 papers, 10 papers with code
GINopic: Topic Modeling with Graph Isomorphism Network
Topic modeling is a widely used approach for analyzing and exploring large document collections.
Automatic Recognition of Learning Resource Category in a Digital Library
Digital libraries often face the challenge of processing a large volume of diverse document types.
Document Image Classification
Optical Character Recognition (OCR)
Hallucination Reduction in Long Input Text Summarization
We have used the following metrics to measure factual consistency at the entity level: precision-source, and F1-target.
CitePrompt: Using Prompts to Identify Citation Intent in Scientific Papers
For the ACL-ARC dataset, we report a 53. 86% F1 score for the zero-shot setting, which improves to 63. 61% and 66. 99% for the 5-shot and 10-shot settings, respectively.
Ranked #2 on
Citation Intent Classification
on ACL-ARC
What Does the Indian Parliament Discuss? An Exploratory Analysis of the Question Hour in the Lok Sabha
The TCPD-IPD dataset is a collection of questions and answers discussed in the Lower House of the Parliament of India during the Question Hour between 1999 and 2019.
Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modeling
Dropout is a widely used regularization trick to resolve the overfitting issue in large feedforward neural networks trained on a small dataset, which performs poorly on the held-out test subset.
Improving Neural Topic Models with Wasserstein Knowledge Distillation
Topic modeling is a dominant method for exploring document collections on the web and in digital libraries.
Improving Contextualized Topic Models with Negative Sampling
Topic modeling has emerged as a dominant method for exploring large document collections.
Abstractive Text Summarization using Attentive GRU based Encoder-Decoder
Indeed automatic text summarization has emerged as an important application of machine learning in text processing.
An Analysis of Abstractive Text Summarization Using Pre-trained Models
People nowadays use search engines like Google, Yahoo, and Bing to find information on the Internet.
Named Entity Recognition Based Automatic Generation of Research Highlights
We investigate whether the use of named entity recognition on the input improves the quality of the generated highlights.
Generation of Highlights from Research Papers Using Pointer-Generator Networks and SciBERT Embeddings
On the new MixSub dataset, where only the abstract is the input, our proposed model (when trained on the whole training corpus without distinguishing between the subject categories) achieves ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 31. 78, 9. 76 and 29. 3, respectively, METEOR score of 24. 00, and BERTScore F1 of 85. 25.
Personal Research Knowledge Graphs
Maintaining research-related information in an organized manner can be challenging for a researcher.
Joint Entity and Relation Extraction from Scientific Documents: Role of Linguistic Information and Entity Types
Scientific articles contain various types of domain-specific entities and relations between them.
Ranked #3 on
Joint Entity and Relation Extraction
on SciERC
Joint Entity and Relation Extraction
Joint Entity and Relation Extraction on Scientific Data
+3
SaSAKE: Syntax and Semantics Aware Keyphrase Extraction from Research Papers
Keyphrases in a research paper succinctly capture the primary content of the paper and also assist in indexing the paper at a concept level.
Segmenting Scientific Abstracts into Discourse Categories: A Deep Learning-Based Approach for Sparse Labeled Data
In the biomedical literature, it is customary to structure an abstract into discourse categories like BACKGROUND, OBJECTIVE, METHOD, RESULT, and CONCLUSION, but this segmentation is uncommon in other fields like computer science.
