A lig N arr: Aligning Narratives on Movies (2021)

Abstract: High-quality alignment between movie scripts and plot summaries is an asset for learning to summarize stories and to generate dialogues. The alignment task is challenging as scripts and summaries substantially differ in details and abstraction levels as well as in linguistic register. This paper addresses the alignment problem by devising a fully unsupervised approach based on a global optimization model. Experimental results on ten movies show the viability of our method with 76% F1-score and its superiority over a previous baseline. We publish alignments for 914 movies to foster research in this new topic.

## introduction motivation and problem. an important aspect of language understanding is the ability to produce a concise and fluent summary of stories, dialogues and other textual contents. automatic text summarization is a long-standing topic in natural language processing @xcite , with numerous approaches for a variety of inputs, largely focusing on news articles and scholarly publications (e.g., @xcite ; @xcite ; @xcite ). in this paper, our focus is on less explored narrative texts such as books and movie scripts. our goal is to automatically align scenes from movie scripts with sentences from plot summaries. such alignments support story browsing and explorative search over screenplays (e.g., find all love scenes), and can also be an asset towards improving summarization and text-generation models for dialogues and other narratives. figure 1 shows an example: a scene snippet from the movie script of shrek, and its corresponding sentence from the plot description of the movie's wikipedia article. establishing this alignment is challenging for three reasons: narrative alignment task: given a script s consisting of a sequence of m scenes {s 1 , s 2 , ..., s m } and a summary u of n sentences {u 1 , u 2 , ..., u n }, the narrative alignment task is to find a mapping between s and u , where both sides can be partial (i.e., some scenes and some sentences are not mapped) and certain constraints are satisfied. prior work and its limitations. the task of aligning narratives across different registers, like script dialogues and plot summaries, has not received much attention before. gorinski and lapata (2015) proposed a graph-based summarization method for movie scripts, exploiting given alignments between script scenes and plot sentences, to select a chain of scenes representing a film's story. their focus was on the generation of the textual summary, and the alignment itself was addressed merely by simple best-match heuristics based on @xcite . nevertheless, as this work is the relatively closest to ours, it is treated as script: suddenly the magic of the spell pulls fiona away. she's lifted up into the air and she hovers there while the magic works around her. suddenly fiona's eyes open wide. she's consumed by the spell and then is slowly lowered to the ground. shrek: (going over to her) fiona? fiona. are you all right? fiona: (standing up, she's still an ogre) well, yes. but i don't understand. i'm supposed to be beautiful. shrek: but you are beautiful. ## approach our alignment method, alignarr, has three steps: ## results and discussion .a small plant in its early stage of growth." combining document relevance scores with sentence similarity scores (alignarr bm25•sts ) results in the best performance with .756 f1 score. adding word overlap scores on top of that (alignarr bm25•sts•w2v ) yields higher precision of .808 but unfortunately at a lower recall rate of .720. detailed comparisons and runtime are available in appendix a and b. we explored different strategies to combine the similarity matrices, and found element-wise matrix multiplication to perform the best.