Movie Plot Analysis via Turning Point Identification

According to screenwriting theory, turning points (e.g., change of plans, major setback, climax) are crucial narrative moments within a screenplay: they define the plot structure, determine its progression and segment the screenplay into thematic units (e.g., setup, complications, aftermath). We propose the task of turning point identification in movies as a means of analyzing their narrative structure. We argue that turning points and the segmentation they provide can facilitate processing long, complex narratives, such as screenplays, for summarization and question answering. We introduce a dataset consisting of screenplays and plot synopses annotated with turning points and present an end-to-end neural network model that identifies turning points in plot synopses and projects them onto scenes in screenplays. Our model outperforms strong baselines based on state-of-the-art sentence representations and the expected position of turning points.

[1]  Jianfeng Gao,et al.  A Human Generated MAchine Reading COmprehension Dataset , 2018 .

[2]  Ahmed Elgohary,et al.  A dataset and baselines for sequential open-domain question answering , 2018, EMNLP.

[3]  Kathleen McKeown,et al.  Extending and Evaluating a Platform for Story Understanding , 2009, AAAI Spring Symposium: Intelligent Narrative Technologies II.

[4]  Snigdha Chaturvedi,et al.  Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships , 2016, NAACL.

[5]  Hiroyuki Shindo,et al.  Wikipedia2Vec: An Optimized Tool for Learning Embeddings of Words and Entities from Wikipedia , 2018, ArXiv.

[6]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[7]  Nan Hua,et al.  Universal Sentence Encoder , 2018, ArXiv.

[8]  Micha Elsner,et al.  Character-based kernels for novelistic plot structure , 2012, EACL.

[9]  Eric T. Nalisnick,et al.  Character-to-Character Sentiment Analysis in Shakespeare's Plays , 2013, ACL.

[10]  Johanna Weiss,et al.  Dictionary Of The Theatre Terms Concepts And Analysis , 2016 .

[11]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[12]  Luca Antiga,et al.  Automatic differentiation in PyTorch , 2017 .

[13]  G. Freytag.  Freytag's Technique of the Drama: An Exposition of Dramatic Composition and Art , 2010 .

[14]  J. Cutting.  Narrative theory and the dynamics of popular movies , 2016, Psychonomic bulletin & review.

[15]  Stefan Feuerriegel,et al.  Adaptive Document Retrieval for Deep Question Answering , 2018, EMNLP.

[16]  Brendan T. O'Connor,et al.  Learning Latent Personas of Film Characters , 2013, ACL.

[17]  Mirella Lapata,et al.  Whodunnit? Crime Drama as a Case for Natural Language Understanding , 2018, Transactions of the Association for Computational Linguistics.

[18]  Robert Wilensky,et al.  An Evaluation of Story Grammars , 1979, Cogn. Sci..

[19]  Chris Dyer,et al.  The NarrativeQA Reading Comprehension Challenge , 2017, TACL.

[20]  Sanja Fidler,et al.  MovieGraphs: Towards Understanding Human-Centric Situations from Videos , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[21]  Kristin Thompson,et al.  Storytelling in the New Hollywood: Understanding Classical Narrative Technique , 1999 .

[22]  Apoorv Agarwal,et al.  Parsing Screenplays for Extracting Social Networks from Movies , 2014, CLfL@EACL.

[23]  Mihai Surdeanu,et al.  The Stanford CoreNLP Natural Language Processing Toolkit , 2014, ACL.

[24]  Jian Zhang,et al.  SQuAD: 100,000+ Questions for Machine Comprehension of Text , 2016, EMNLP.

[25]  Timothy Baldwin,et al.  Towards Automatic Animated Storyboarding , 2008, AAAI.

[26]  Yoshua Bengio,et al.  HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering , 2018, EMNLP.

[27]  Mirella Lapata,et al.  Movie Script Summarization as Graph-based Scene Extraction , 2015, NAACL.

[28]  Sebastian Riedel,et al.  Constructing Datasets for Multi-hop Reading Comprehension Across Documents , 2017, TACL.

[29]  David Bamman,et al.  A Bayesian Mixed Effects Model of Literary Character , 2014, ACL.

[30]  Gabriel Stanovsky,et al.  DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs , 2019, NAACL.

[31]  Sanja Fidler,et al.  MovieQA: Understanding Stories in Movies through Question-Answering , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Jon M. Kleinberg,et al.  You Had Me at Hello: How Phrasing Affects Memorability , 2012, ACL.

[33]  Nathanael Chambers,et al.  Unsupervised Learning of Narrative Schemas and their Participants , 2009, ACL.

[34]  Philip Bachman,et al.  NewsQA: A Machine Comprehension Dataset , 2016, Rep4NLP@ACL.

[35]  Snigdha Chaturvedi,et al.  Unsupervised Learning of Evolving Relationships Between Literary Characters , 2017, AAAI.

[36]  Mirella Lapata,et al.  What’s This Movie About? A Joint Neural Network Architecture for Movie Content Analysis , 2018, NAACL.

[37]  Gerard Salton,et al.  Automatic text decomposition using text segments and text themes , 1996, HYPERTEXT '96.

[38]  Yejin Choi,et al.  Connotation Frames of Power and Agency in Modern Films , 2017, EMNLP.

[39]  Shrikanth S. Narayanan,et al.  A quantitative analysis of gender differences in movies using psycholinguistic normatives , 2015, EMNLP.

[40]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[41]  Marti A. Hearst.  Text Tiling: Segmenting Text into Multi-paragraph Subtopic Passages , 1997, CL.

[42]  Kai Liu,et al.  Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification , 2018, ACL.

[43]  Eunsol Choi,et al.  TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension , 2017, ACL.

[44]  Jason Weston,et al.  Reading Wikipedia to Answer Open-Domain Questions , 2017, ACL.

[45]  Apoorv Agarwal,et al.  Key Female Characters in Film Have More to Talk About Besides Men: Automating the Bechdel Test , 2015, HLT-NAACL.