---
language: en
tags:
- Summarization
- Abstractive Summarization
model-index:
- name: kubershahi/pegasus-inshorts
  results:
  - task:
      type: abstractitive summarization
      name: abstractive summarization
    dataset:
      name: inshorts
      type: inshorts
      config: inshorts
      split: train
    metrics:
    - name: ROUGE-L-P
      type: rouge
      value: 0.01074
      verified: true
    - name: ROUGE-L-R
      type: rouge
      value: 0.08284
      verified: true
    - name: ROUGE-L-F
      type: rouge
      value: 0.08284
      verified: true
    - name: ROUGE-1-P
      type: rouge
      value: 0.01074
      verified: true
    - name: ROUGE-1-R
      type: rouge
      value: 0.08284
      verified: true
    - name: ROUGE-1-f
      type: rouge
      value: 0.08284
      verified: true
license: mit
datasets:
- kubershahi/inshorts
metrics:
- rouge
---


# Problem Statment: 

Given a news article, generate a summary of two-to-three sentences and a headline for the article. The summary should be abstractive rather than extractive.
In abstractive summarization, new sentences are generated as part of the summary and the sentences in the summary might not be present in the news article.


# Model Description

This model builds on the [google/pegasus-large](https://huggingface.co/google/pegasus-large) model by finetuning it on a custom summary-headline dataset called [inshorts](https://github.com/kubershahi/ashoka-aml/blob/master/dataset/news_headline.csv).
After finetuning, to generate an appropriate headline of an article, get the summary of the article first from the pegasus-large model and then pass the summary through this model. 
The two-way approach was taken to get apt headline from summary rather then generating the headline from the pegasus-large itself. 


For more details about the project, click [here](https://github.com/kubershahi/ashoka-aml).