NLP Summary2Dialogue

Character Pipeline

• Character Detector : Used a combination of both traditional NER (NLTK) and neural net pretrained NER (spacy) to detect all possible characters incase any -one of them missed out some entity.
• Character Recogniser : To understand the characters and related context, find all mentions/expressions in the summary and link the pronouns with their nouns (Coreference Resolution).

Character Mapping

• Here we extract dialogue from the script for each character and convert it into the corresponding vector using word2vec. You need to download this file to use the word2vec model

Script GPT

• We finetune GPT2 using data from imsdb. We use the imsdb_scrapper for scraping the data from the website.

example_1917

• Example of using Finetuned GPT2 to generate scripts from summary