Multi-view Story Characterization from Movie Plot Synopses and Reviews (2020)

Abstract: This paper considers the problem of characterizing stories by inferring properties such as theme and style using written synopses and reviews of movies. We experiment with a multi-label dataset of movie synopses and a tagset representing various attributes of stories (e.g., genre, type of events). Our proposed multi-view model encodes the synopses and reviews using hierarchical attention and shows improvement over methods that only use synopses. Finally, we demonstrate how we can take advantage of such a model to extract a complementary set of story-attributes from reviews without direct supervision. We have made our dataset and source code publicly available at https://ritual.uh.edu/multiview-tag-2020 .

## introduction a high-level description of stories represented by a tagset can assist consumers of story-based media (e.g., movies, books) during the selection process. although collecting tags from users is timeconsuming and often suffers from coverage issues @xcite , nlp techniques like those in @xcite and @xcite can be employed to generate tags automatically from written narratives such as synopses. however, existing supervised approaches suffer from two significant weaknesses. firstly, the accuracy of the extracted tags is subject to the quality of the synopses. secondly, the tagset is predefined by what was present in the training and development sets and thus is brittle; story attributes are unbounded in principle and grow with the underlying vocabulary. to address the weaknesses presented above, we propose to exploit user reviews. we have found that movie reviews often discuss many aspects of the story. for example, in figure 1 , a reviewer writes that the godfather is about family, rela- ## background prior art related to this paper's work includes story analysis of movies and mining opinions from movie reviews. in this section, we briefly discuss these lines of work. ## dataset our starting data set is the mpst corpus @xcite which contains approximately 15k movies and a set of tags assigned by imdb 1 and movie-lens 2 users. the tagset contains 71 labels that are representative of story related attributes (e.g., thought-provoking, inspiring, violence) and the corpus is free from any metadata (e.g., cast, release year). we extended the dataset by collecting up to 100 most helpful reviews per movie from imdb. out of the 15k movies in mpst, we did not find any reviews for 285 films. the collected reviews often narrate the plot summary and describe opinions about movies. we noticed that reviews can be very long and sometimes contain repetitive plot summaries and opinions. some reviews can be even uninformative about the story type. moreover, the number of reviews for movies varies greatly, creating a challenge for modeling them computationally. so we summarize all reviews for a movie into a single document using textrank foot_2 @xcite . we observed that summarized reviews are usually free of repetitive information and aggregate the salient fragments from the reviews that are heavy with user opinions. all plot synopses, reviews, and tags are in english, and ## experiments we treat our tag assignment task as a multi-label classification problem. based on p (y p |x), we sort the predefined tagset y p in descending order, so that tags with higher weights are ranked on top. then, in different settings we select the top-k (@xmath0, 5) tags as the final tags to describe each movie. we aim to explore three research questions through our experiments: (q1) for predicting tags from synopses only, can our approach outperform other machine learning models? (q2) when available, can reviews strengthen the synopses to tag prediction model? and (q3) how relevant are open-vocabulary tags to stories? for q1 and q2, we evaluate systems based on two aspects: a) correctness of top-k predictions by micro-f1, and b) diversity in top-k tags using tags learned (tl; @xcite . tl is simply the number of unique tags predicted for the entire evaluation set in top-k setting; i.e., |y k p pred |. we verify q3 through a human evaluation experiment as we do not have annotations for review tags. ## conclusion in this paper, we focused on characterizing stories by generating tags from synopses and reviews. we modeled the problem from the perspective of multiple instance learning and developed a multi-view architecture. our model learns to predict tags by identifying salient sentences and words from synopses and reviews. we demonstrated that exploiting user reviews can further improve performance and experimented with several methods for combining user reviews and synopses. finally, we developed an unsupervised technique to extract tags that identify complementary attributes of movies from user reviews. we believe that this coarse story understanding approach can be extended to longer stories, i.e., entire books, and are currently exploring this path in our ongoing work. we tokenize the synopses and reviews using spacy foot_8 nlp library. to remove rare words and other noise, we retain the words that appear at least in ten synopses and reviews (< .01% of the dataset). additionally, we replace the numbers with a cc token. through these steps we create a vocabulary of ≈42k word tokens. we represent the out of vocabulary words with @xmath1. for each movie sample, there are two text inputs (written synopsis and summary of reviews). we use an empty string as the review text for the movies not having any review.