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Science journalism meets artificial intelligence "Robotic Journalism"

By - Raghuram Vadapalli, Bakhtiyar Syed, Nishant Prabhu , Balaji Vasan Srinivasan, Vasudeva Varma.
Summary of Research papers from IIT Hyd





 Since couple of years an exciting topic is getting attraction in Machine learning and Artificial world that is "Robot Reporter".  Today's paper got inspired by the concept. Application to science journalism is non-trivial, as that would entail understanding scientific content and translating it to simpler language without distorting underlying semantics. paper heads infant steps towards answering few challenges.

 Authors came out with a tool, which, given the title and abstract of a research paper will generate a blog title by mimicking a human science journalist. The tool uses model trained on 87,328 pairs of research papers and their related blogs.



Contributions can be summed up as follows


1. A new parallel corpus of 87, 328 pairs of research paper titles and abstracts and their corresponding blog titles.

2. Demonstrating the web application, which uses a pipeline-based architecture that can generate blog titles in a step-by-step fashion,while enabling the user to choose between various heuristic functions as well as the neural model to be used for generating the blog title.

3. Analyzing the outcomes of the experiments conducted to find the best heuristic function as well as network architecture.

Architecture 





Stage 1 


 uses heuristic function to analyse and extract sequence

 What is Heuristic function...

 The Heuristic function is a way to inform the search about the direction to a goal. It provides an informed way to guess which neighbor of a node will lead to goal. There is nothing magical about heuristic function. It must use only information that can be readily obtained about node.

Stage 2 


 The pointer-generator model used to generate the output sequence from the intermediate sequences.

 Sequence to Sequence (seq2seq) is a learning model that converts an input sequence into output sequence. Seq2Seq model has achieved great success in fields such as machine translation, dialog systems, question-answering.

Blog Title Generation

Heuristic functions takes title and abstract of research paper as input H(pt, abs) where pt is paper title and abs is paper abstract. Various heuristic functions were explored and are outlined below
1)pt
2)RP (TF-IDF based)
3)RD (Flesch reading ease based)
4)RPD (normalized of RD and RP)
The output of the previous step is fed into a sequence-to-sequence neural generation model in order to generate the title of the blog post.

 System provides a baseline attention network which defines 'attention' over the input sequence to allow the network to focus on specific parts of the input text and the pointer-generator
The sequence s obtained from the first stage is the input to the neural natural language generation model which generates bt' as output with loss function  L(bt, bt'), given by sum of cross entropy loss at all time-steps:








Working prototype gives opportunity to play around with combination of heuristic functions and model types for generating blog title

 Link for working site


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