The question behind the work
Summaries need to retain important information while becoming much shorter. These experiments explore that question across Indian languages and different model families.
LANGUAGE / SUMMARIZATION
What survives a summary?
A collection of Indian news summarization experiments with transformer fine-tuning and LLM inference.
THE PROCESSINPUT → UNDERSTANDING
01Sample
Prepare news examples from XLSum.
02Generate
Fine-tune a transformer or run LLM inference.
03Compare
Inspect the generated summaries.
04Evaluate
Examine quality across languages and approaches.
THE PROJECT
Multilingual Summarization
Collection of related implementations
How it comes together
The collection combines IndicBART transformer experiments with GPT-4o Mini and Cohere Command R+ inference. English, Hindi, Gujarati, Bengali, Telugu and Tamil appear across the collection; individual notebooks vary in coverage.
What to keep in mind
The notebooks document related experiments, rather than a single uniform benchmark. Consult each implementation for its language coverage and configuration.
Language changes.
The question remains.
A small, prewritten language illustration. It does not run the project’s models or report experimental results.
SOURCE TEXT
SHORT SUMMARY
Illustrative examples · inspect the notebooks for generated outputs and evaluation.