Transformer-based Screenplay Summarization Using Augmented Learning Representation with Dialogue Information (2021)

Abstract: Screenplay summarization is the task of extracting informative scenes from a screenplay. The screenplay contains turning point (TP) events that change the story direction and thus define the story structure decisively. Accordingly, this task can be defined as the TP identification task. We suggest using dialogue information, one attribute of screenplays, motivated by previous work that discovered that TPs have a relation with dialogues appearing in screenplays. To teach a model this characteristic, we add a dialogue feature to the input embedding. Moreover, in an attempt to improve the model architecture of previous studies, we replace LSTM with Transformer. We observed that the model can better identify TPs in a screenplay by using dialogue information and that a model adopting Transformer outperforms LSTM-based models.

## introduction text summarization is one major task in nlp that seeks to produce concise texts containing only the essential information in the original texts. although most researches have been focusing on summarizing news articles @xcite , as various contents with different structures increase these days, there has been growing interests in applying text summarization to various domains, including social media @xcite , dialogue @xcite , scientific articles @xcite , books @xcite , screenplays (or scripts) @xcite . among them, this paper focuses on screenplay summarization. a screenplay is a type of literary text, which typically contains around 120 pages and has a strictly structured format (figure 1 ). it usually contains various storytelling elements, such as a story, dialogues, characters' actions, and what the camera int. sid's room woody we're gonna get outta here, buzz -buzz? buzz is not there. woody looks down at the floor. buzz is sitting on the floor, playing "bombs away" with his broken arm. ## conclusion in this paper, we suggest using dialogue information as an additional training feature and propose a transformer-based architecture for tp identification. our experimental results present that dialogue information has a positive effect on the prediction accuracy on whether the scene is tp or not. however, the opposite was the case for the sequence-based model; further analyses are needed. in addition, the results indicate that using transformer instead of lstm significantly improves the overall performance in identifying tp scenes by encoding long-term dependencies among scenes better. we believe that using unique attributes in screenplays, such as dialogues, can help improving the model performance and when summarizing texts that have complex structures including screenplays, transformer , which handles long histories robustly, is effective. in the future, we plan to go through the human evaluating process to see how dialogue information affects the output summary's informativeness, especially which one is identified better than another, and how the trade-off among automatic evaluation metrics affects the summary output.