PW Interview: Jolo Balbin Co-Founder Text Teaser

Ajay Ohri, September 18th, 2013

Here is an interview with Jolo Balbin, co-founder of Text Teaser. TextTeaser is an automatic summarization application and API. It uses an algorithm combines the power of natural language processing and machine learning technologies.

In the API, TextTeaser accepts the URL or the title + text of the article. It will then be processed using the algorithm to generate a summary. There are also optional parameters that can be added: blog, and category of the article. These optional parameters are used to improve the learning capability of the algorithm and provide a better summary.

PW-  Please describe your background in NLP and Machine Learning.

Jolo- I took Natural Language Processing and Machine Learning courses when I was studying for my undergraduate degree. I then pursue NLP and Machine Learning in our undergraduate thesis. It is about automatic summarization for Senate Bills. My knowledge in NLP and Machine Learning widens when I took my MS degree and do a research about automatic summarization for news articles. Right now, I’m working as a Data Scientist at Bright.com and still learning new stuff about NLP and Machine Learning.

PW- Describe the process of how you came up with the idea and then made an API startup. What are some of things you wished you knew before?

Jolo- My interest in Automatic Summarization started when we are doing our an automatic summarization for Senate bills. We successfully built a good system for it but we failed in applying it into the real world. With it, I saw something in automatic summarization and decided to pursue it in my graduate studies. I focused on news articles and created a good algorithm for it. I don’t want to fail in turning it into real world application twice and that’s why I created TextTeaser.

PW- One more Automatic Summarization application? How does TextTeaser stand out in the NLP market in terms of performance and technology?

Jolo- TextTeaser uses a special algorithm that was formulated during my research in my graduate studies. I also did several test comparing it to different summarization algorithms. It turns out to be better. TextTeaser also has an ability to learn. It employs a machine learning technique and will get better as more articles summarized. Additionally, it got a lot of positive feedback from different people in Reddit, Medium, and Hacker News.

PW-  What is your pricing strategy for your API? How do you intend to make developers excited about making apps and mashups with TextTeaser ?

Jolo- Right now, we have a freemium pricing strategy. We offer free limited API access per month, and will just charge if there are more articles to be summarized. More information about it in our pricing page at Mashape (https://www.mashape.com/mojojolo/textteaser#!pricing). We also offer free trials to those who are interested in trying TextTeaser, they can always message me. Actually, there are students and researchers that messaged me to use TextTeaser in their research and experiments.

PW-  What are your views on how the future of NLP in terms of technology and business landscape? What are some of the core technologies you track or keep an eye on?

Jolo- With data got bigger and bigger, hence big data, more and more technologies will be used to manage them. I think the rise of Natural Language Processing and Machine Learning is starting. There are lots of companies employing those technologies. I saw job pages of Facebook and Twitter specifically searching for those technologies. I’m also seeing companies to utilize genetic algorithms, neural networks, and many more.

In terms of NLP, I’m really looking forward to automatic summarization by abstraction. I think right now, there is still no good automatic summarization that do abstraction. But I’m sure, there will be in the near future. I’m excited how it can imitate the style of summarization done by humans… or maybe better. It will be a pretty exciting future.

You can also check out the Text Teaser API in Mashape or see it in action here.

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