Text Summarization using a custom built deep learning algorithm. Generating effective summary for any large document using Deep Learning is a hot research topic. In the market we have seen many keywords based extraction methods which gets your job partially done but the summary is not like we humans write. We were tasked to build a model which would be used to generate summaries like Abstracts in a research paper using an abstractive summarization method.
Key Technical Challenges:
- A custom deep learning model which can understand the meaning of a document & can modify certain statements based on vocabulary given with mathematical equations taken into account.
- Gathering enough data & re-structuring it in a way that model can understand it.
- Generating summary the way the client needed.
Business + Technical Points:
- Accurate enough summary which has the meaning of document kept & dynamic in nature based on user’s desired characters.
- System should be scalable automatically to get new kinds of data into training & consideration.
- As research papers have similar kind of structure, there is little need for training down the line i.e. make the model more generalized.
- We are able to summarize a long document into a short summary with math equations taken into summary as well.