Summarizing Indian Languages using Multilingual Transformers based Models

29 Mar 2023 · Dhaval Taunk, Vasudeva Varma ·

With the advent of multilingual models like mBART, mT5, IndicBART etc., summarization in low resource Indian languages is getting a lot of attention now a days. But still the number of datasets is low in number. In this work, we (Team HakunaMatata) study how these multilingual models perform on the datasets which have Indian languages as source and target text while performing summarization. We experimented with IndicBART and mT5 models to perform the experiments and report the ROUGE-1, ROUGE-2, ROUGE-3 and ROUGE-4 scores as a performance metric.

PDF Abstract

Code

Add Remove Mark official

No code implementations yet. Submit your code now

Tasks

Add Remove

Datasets

Add Datasets introduced or used in this paper

Results from the Paper

Edit

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods

Add Remove

Adafactor • Attention Dropout • BPE • Dense Connections • Dropout • GELU • GLU • Inverse Square Root Schedule • Layer Normalization • Linear Layer • mBART • mT5 • Multi-Head Attention • Residual Connection • Scaled Dot-Product Attention • SentencePiece • Softmax • T5

Edit Social Preview

Summarizing Indian Languages using Multilingual Transformers based Models

Code Edit Add Remove Mark official

Tasks Edit Add Remove

Datasets Edit

Results from the Paper Edit

Methods Edit Add Remove

Code

Add Remove Mark official

Tasks

Add Remove

Datasets

Results from the Paper

Edit

Methods

Add Remove