Analyzing Film Adaptation through Narrative Alignment
Authors: Tanzir Pial
Shahreen Salim Aunti
Charuta Pethe
Allen Kim
Steven Skiena
Venue: EMNLP 2023 Main (2023)
Keywords: Text Alignment
Book Movie Alignment
TL;DR: We analyze the book to film adaptation process through aligning source book text with the the film adaptation's script.

Abstract: Novels are often adapted into feature films, but the differences between the two media usually require dropping sections of the source text from the movie script. Here we study this screen adaptation process by constructing narrative alignments using the Smith-Waterman local alignment algorithm coupled with SBERT embedding distance to quantify text similarity between scenes and book units. We use these alignments to perform an automated analysis of 40 adaptations, revealing insights into the screenwriting process concerning (i) faithfulness of adaptation, (ii) importance of dialog, (iii) preservation of narrative order, and (iv) gender representation issues reflective of the Bechdel test.

Analyzing Film Adaptation through Narrative Alignment
Tanzir Pial, Shahreen Salim, Charuta Pethe, Allen Kim, Steven Skiena
Department of Computer Science, Stony Brook University, NY, USA (tpial, ssalimaunti,,peehe,, allekim, skiieaa@esssttonbbrrdd edu
Abstract
tation process.
Novels are often adapted into feature films, but the differences between the two media usu- ally require dropping sections of the source text from the movie script. Here we study this screen adaptation process by constructing nar- rative alignments using the Smith- Waterman local alignment algorithm coupled with SBERT embedding distance to quantify text similarity between scenes and book units. We use these alignments to perform an automated analysis of adaptations, revealing insights into the screenwriting process concerning (i) faithful- ness of adaptation, (ii) importance of dialog, (iii) preservation of narrative order, and (iv) gender representation issues reflective of the Bechdel test.
The importance of book-to-film adaptation is evi- denced by its economic and cultural impact. Fron- tier Economics (2018) found that books were the basis of more than 50% of the top 20 UK-produced films between 2007-2016. These films grossed f22.55n globally, accounting for 65% of the global box office gross of the top UK films. Recent avail- ability of large-scale book and screenplay data em- powers the NLP community to study the screenwrit- ing process. Our work opens new lines of research in this domain, providing insight into the thought processes of screenwriters and film producers.
The relationship between book source and film script has repcatedly been the subject of research within the NLP community. The degree to which movie adaptations are faithful to the original books was raised by Harold (2018), who considers two questions: how it can be decided if a movie is faith- ful to its book, and whether being faithful to its source is merit for movic adaptation. There have been many works focusing on analyzing movie adaptations of specific books, such as Rahmawati et al. (2013) and Österberg (2018).
1 Introduction
Book-to-film adaptation plays an important role in film-making. From artistic point of view, there are many possible ways to craft a movic from any given book, all of which entail changes (addition, deletion, and modification) to the structure of the text. Books and films are different forms of media, with different characteristics and goals. Novels are generally much longer texts than screenplays, writ- ten without the strict time-limits of film. Further, film adaptations present the screenplay writers' in- terpretation of the book, whose intentions are often different from the author of the original text.
With the advent of GPT-3 (Brown et al., 2020) and more advanced models, Artificial Intelligence (AI) systems are increasingly being proposed auto- mate creative tasks in the entertainment industry.
this paper, we study which parts of books get excised from the script in their movie adaptations. Our technical focus is on methods to identify align- ments between each unit (e.g., dialog, set-up of a scene) of movie script with the paragraphs of the book. However, certain book paragraphs must be left unmatched when appropriate, reflecting sec- tions of the book deleted in the movie adaptation for simplifying or focusing the action. We primar- ily focus on understanding screenwriter behavior and discovering patterns in the book-to-filn adap-
The Writers Guild of America's 's historic strike high- lighted concerns about Al's use in covered projects specifically, there was contention that Al should not be involved in the direct writing or rewriting of literary material, nor should it be utilized as source material (WGA, 2023). A book to film script align- ment and analysis tool has potential application for both sides of the debate, e.g., by creating training dataset for generative models for rewriting creative materials into different formats and analyzing the differences between materials created by humans

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(a) Movie: HP7 Part
(b) Movie: HP7 Part 2
Figure 1: When one book becomes two movies. Aligmments of the seventh Harry Potter book (top line) with scripts for the Part (left) and Part (right) scripts. The green edges denote alignment with the correct portion of the book, opposed to the magenta edges. Our local alignment methods recognize the split structure of the book much better than global alignment methods like Book2Movie (Tapaswi et al., 2015), who align only 41.4% of Part with the second part of the book, compared to 81.6% with our alignment.
and AI, as well as identifying areas where Al may have limitations.
Recognizing Faithfulness of Adaptations Certain film adaptations retain only the title of the putative source book, while others strive fully capture the contents and spirit of the source. We use our alignment methods define faithfulness statistic (retention per- centage), and demonstrate its performance on 18 book-script pairs where published critics have commented on the faithfulness of the adaptation. We achieve Spearman rank cor- relation of 0.65 between retention percentage and critic labels, significant to the 0.001 level.
We frame the book-to-scripl problem as sequence alignment problem, where book and its movie script are two text sequences. Sequence alignment techniques are critically important in bioinformat- ics, specifically for efficiently aligning genome and protein sequences. In the field of computer vision, some of these algorithms have been adapted for image matching (Thevenon et al., 2012). Pial and Skiena (2023) posit that these algorithms have role to play in the analysis of long texts in NLP too. We adapt their proposed text alignment methods for the book-to-film adaptation domain. The primary mechanism of most sequence alignment algorithms does not change based on the components of the sequence. Provided with a definition for similarity (or distance) between individual components, they can be adapted to any domain.
Investigations of Screemwriter Behavior For 40 book-film adaptations, we analyze screen- writer decisions from several perspectives:
We demonstrate that book-units with di- alog are substantially more likely to be retained than other narrative text, signifi- cant with p-valuc < 3 x 10-6
Our primary contributions' include:
We prove that the narrative order of book- script pairs are highly consistent, demon- strating that local Smith- Waterman align- ments capture global narrative order.
Book-Script Alignment Algorithm To iden- tify commonalities between a book and its script adaptation, we introduce a domain- adapted book-to-film alignment approach. This method builds upon the techniques ad- vanced by Pial and Skiena (2023) for gen- eral narrative alignment. It leverages mod- ern text embedding methodologies in conjune- tion with local alignments generated by the Satth-Waterman (SW) algorithm (Smith and Waterman, 1981), a seminal technique in com- putational biology. Our approach proves sig- nificantly more accurate than prior methods including BookeMovie (Tapaswi et al., 2015). Our alignments identify which portions of the book are excised in the adaptation, providing novel insights into the screenwriting process.
We show that script alignments can re- vcal whether the adaptation is likely to pass the gender-role sensitivity Bechdel test based on text from given book.
Datasets for Text-to- Text Aligument We in- troduce three novel datasets designed to facili- tate research on the book-to-movie alignment task. The first consists of six book-script pairs where each unit of the book is annotated as either retained removed in the correspond- ing screenplay. The second dataset contains two book-script pairs where we fully align individual chapters of the book with the cor- responding scenes in the movie. Finally, we provide a dataset of 40 book-seript pairs to ex-
'll codes and datascts are available at https::/ //ttps://github com/ hom//anzzr5/// wtaeeiii//aaagggge htwp//mnnnn.....mam/////m/am...m @otomenn__toll_ee_

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plore additional research questions associated with film adaptation. Our software, testing procedures, and data resources will be made publicly available upon acceptance of this pa- per, subject to copyright limitations on the original data.
Method
IIP2
HIP4
Complexity
Baseline (Length)
22.6
O(c+s)
Book2Movie Greedy + SBERT
59.5
52.5
O(c*s log(())))))
61.2
51.5
O(ma log log(mn) (n))
SW + SBERT
85.3
66.2
O(mn log(mn)
Table I:
Comparison of alignment accuracy and effi- ciency of different algorithms. For Baseline (Length) we align each chapter sequentially with a number of scenes one by one according to the length of the chap- ters. = no. scenes, c = no. of chapters, m = no. of book paragraphs, no. of scene paragraphs.
The outline of this paper is follows. We survey work on script analysis and alignment methods in Section 2. Our alignment methods based on Smith- Waterman and SBERT embeddings are presented in Section 3. and the datasets we develop in Section 4, The accuracy of our alignment algorithms is estab- lished in Section 5, before we apply these methods to four aspects of film adaptation in Section 6.
Doc2 Vec (Le and Mikolov, 2014) embeddings for creating monolingual parallel corpus composed of the works of English early modern philosophers and their corresponding simplified versions.
2 Related Work
The task of aligning the video of a movie with a book has been explored in prior research. Tapaswi et al. (2015) proposed a graph-based approach to align book chapters with corresponding movie scenes, treating the alignment as a shortest path finding problem. Zhu et al. (2015) utilized neural sentence embeddings and video-text neural embed- dings to compute similarities between movie clips and book sentences. contrast, our work primar- ily analyzes the removed and retained parts of the book to gain insights into screenwriter behavior while also performing book-to-film alignment.
Additionally, several previous works have ad- dressed different aspects of film scripts, such genre classification (Shahin and Krzyžak, 2020), estimating content ratings based on the language use (Martinez al., 2020), script summarization (Gorinski and Lapata, 2015), sentiment analysis (Frangidis et al., 2020), automated audio descrip- tion generation (Campos et al., 2020), creating dia- log corpora (Nio et al., 2014), creating multimodal treebanks (Yaari et al., 2022). Lison and Meena (2016) performs subtitle movic script alignment for creating training dataset for automatic turn seg- mentation classifier. However, the best of our knowledge, ours the first-ever work on automated analysis of book-to- book-to-film filfilm adaptations on a moder- ately large scale (40 books).
Core to proposed method is the dynamic pro- gramming (DP) algorithm for alignment. DP and the associated dynamic time warping algorithms have been widely applied in various film alignment tasks, including aligning script dialogs with tran- script dialogs (Everingham et al., 2006; Park et al., 2010), aligning scripts with video for person iden- tification (Bauml et al., 2013), action recognition (Laptev al., 2008), and identifying scene order changes in post- productttnt (Lambert et al., 2013), aligning plot synopses with video (Tapaswi al., 2014), and annotating movie videos with script de- scriptions (Liang et al., 2012). Kim et al. (2020) uses DP to segment novels into parts corresponding to a particular time-of-day.
In bioinformatics, sequence alignment has been studied in depth for many decades. Accurate but comparatively slower classes of algorithms in- clude the DP algorithms like local alignments by Smith and Waterman (1981), global alignments by Needleman and Wunsch (1970), etc. Global align- ments align the whole sequence using DP, while local alignments try to align locally similar regions.
In this work, we use Smith-Waterma (SW) to pro- duce local alignments, which are subsequently used to construct the final global alignment. Heuristic but faster algorithms include Altschul et al. (1990).
Neural machine translation (Bahdanau et al., 2014) models create soft alignments between target words and segments of the source text relevant for pre- dicting the target word. The size of the book and film scripts restrict us to directly employ neural models like this. Other than translations involv- ing two languages, Paun (2021) proposes using
Aligning Books with Film Scripts
Here we describe the Smith- Waterman algorithm for local sequence alignment, including the similar- ity metrics we use to compare books and scripts.

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3.1 The Smith-Watermaa Algorithm
alignment, each scene paragraph with an alignment casts a vote for the chapter of the aligned book paragraph. We align the chapter with the maximum number of votes with the scene.
We modify the classical Smith- Waterman algo- rithm (SW) to allow for many-to-many matching, since paragraph in the book can be conceptually aligned with multiple units of the script. We calcu- late the highest alignment score for two sequences X and Y of length m and n, respectively, using the recurrence relation H [i.
3.3 Similarity Metric
We use paragraphs as the smallest unit for align- ment and generate semantically meaningful embed- dings for each book and script paragraph using the pretrained SBERT model (Reimers and Gurevych, 2019). While SBERT is primarily designed for sen- tence embeddings, it has been trained diverse datasets for various purposes. We use the SBERT model pretrained on the MS MARCO (Nguyen et al., 2016) dataset for information retrieval. This particular model is trained to maximize cosine sim- ilarity between relevant query and passage pairs. Given that script paragraphs typically consist of one to two sentences resembling single-sentence queries in the training data, and book paragraphs are generally longer akin to multi-sentence pas- sages in the training data, this model well-suited for our method.
Hii L,j - 1) + S(X,, Yj), H(i 1,j) + + max (0, SX,, Kj),,
H(,)) max
H(i.) 1) +-+ max(0, S(X,, Yj)), 0
(1)
To identify the most similar segment pair, the scching- cell in the SW DP matrix is located, and segments are extended left towards the front of the sequences until zero-score cell is reached. The contiguous sub-sequence from the start cell to the zero-score cell in each sequence forms the most similar segment pair.
The algorithm allows for matching two components using a similarity metric or deleting a component with a gap penalty, g. We set g = -0.7 empirically. The algorithm has time complexity of (mm)..
Dayhoff et al. (1978) suggests that DP based algo- rithms perform well when the expected similarity score of two random unrelated components is neg- ative and only related components have a positive similarity score. To incorporate this idea, we com- pute the similarity score between two paragraphs x and y as,
3.2 Independent Matches in Local Alignments
A complete book-script alignment consists of col- lection of local alignments, but there is no single standard strategy in the literature for creating global collections from local alignment scores. Here we use greedy approach, processing matching seg- ments in decreasing alignment score order. To clim- inate weak and duplicate local alignments, skip paragraph pairs. The cost of sorting the DP matrix scores results in an algo- rithm with Omm log(mn)) time complexity.
S(x,y) = oZ((z,))) th.) x 2 2-1 (2)
where (.) denotes the logistic sigmoid function, [(., .) denotes the z-score of the cosine similarity between r and y derived from a distribution of sim- ilarity scores between random paragraphs, and th, denotes a threshold for having a positive score. The threshold the ensures that all pairs with less than the z-score have a negative similarity score. We set the to +0.6 empirically, but found that all thresh- olds from +0.5 to +1 z-score performed similarly.
over previously-selectee
Using the paragraph-level alignments produced by SW, compute information at two different lev- els of granularity: decision on the removal of book units (usually made up of 5-10 paragraphs, as dis- cussed in Section 4. and alignment of chapters (made up of 25-50 paragraphs). Generally, book units prove a more suitable choice. Conversely, aligning chapters with scenes provides univer- sally recognized evaluation method employed in previous works (Tapaswi et al., 2015).
4 Datasets
This study focuses on three data sets of annotated book-script pairs:
Removed Parts Annotation Set. For six book-script pairs (four Harry Potter and two Jane Austen classics), we manually annotated
We interpret the removal of book unit from the script whenever less than half of its paragraphs align with any scene paragraph. In case of chapter
hnttps:/wwww.Shr wwt.eett..//oooccrrrrrraaeeeeeee msmarco-v3.htm html

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Alignment Method
Similarity Metric
PB
HP2
HP4
HPРР1
HP7P2
PP
jaccard
0.64
0.34
0.38
glove
0.58
0.28
0.31
Greedy (Baseline)
hamming
0.51
0.12
0.35
tf-idf
0.63
0.32
0.32
0.64
SBERT
0.72
0.40
0.45
jaccard
0.60
0.72
0.45
0.73
0.63
0.6
Smith-
glove
0.56
0.58
0.27
0.37
0.33
0.54
Waterman
hamming
0.42
0.48
0,30
0.24
0.20
0.47
(SW)
tf-idf
0.59
0.68
0.41
0.64
0.55
0.55
SBERT
0.67
0.73
0.48
0.74
0.73
0.65
Table 2: Fl scores of different methods determine whether book unit is retained removed the film adaptation. SBERT+SW achieves the highest F1 score on six of seven books. HP denotes the Harry Potter series by JK Rowling while PB and denote The Princess Bride by William Goldman and Pride and Prejudice by Jane Austen respectively
each book unit as retained or removed. They are listed Table A. of the appendix. We note that the dataset contains five books, not six, because two movies were adapted from the seventh Harry Potter book (HP7).
bit dialog or set-up of the scene.
In contrast, books generally lack such rigid struc- ture. While chapters serve a common unit of segmentation for books, they are often too lengthy for analyzing retention statistics. To investigate the scriptwriter's decisions regarding the retention book parts, it becomes necessary to segment books into smaller units. We adopt the text segmentation algorithm proposed by Pethe al. (2020). This al- gorithm leverages weighted overlap cut technique identify local minimas breakpoints where the word usage transitions to a different subset of vo- cabulary indicating context switch. These result- ing text units are referred to as book units.
Manual Alignment Set. We manually aligned two books with their movie adaptation scripts. We aligned each scene with a chapter of the book. The two book-script pairs are two volumes of the Harry Potter series (HP2 and HP4, books also used in the first dataset).
Automated Analysis Set. We collected 40 book-script pairs for automated analysis from mix of Gutenberg (n.d.), the HathiTrust Dig- ital Library (n.d.), and personal sources. Infor- mation about these books is provided in Table B.1 where each book-film is given unique ID and Table B.2 the appendix.
Evaluation
We evaluate the performance of our alignment algo- rithm against several baselines and previous work. There are two distinct aspects to be considered our evaluation: (i) the performance of different text similarity metrics, and (ii) local vs. global align- ment approaches such as Book2Movie (Tapaswi et al., 2015). Additionally, we provide qualitative evaluation of our alignments in Appendix C.
We collected the scripts from popular websites that publicly host scripts using the code developed by Ramakrishna al. 2). The length of books in pages and associated films in minutes have Pear- son correlation of 0.36, indicating that longer books get made into longer films. The process of manual annotation involved multiple researchers, but each book-movie pair was annotated by single person without any involvement of other annotators.
5.1 Baselines
Different Similarity Metrics:
To test the impact of similarity metric performance, five different versions of SW, using the similarity metrics of Jaccard similarity, word embedding- based metric (GloVE), Hamming distance, TF-IDF and SBERT, respectively. Details of these metrics appear in Appendix D.
evaluate
4.1 Segmenting Texts
Film scripts generally follow typical format with slug lines at the beginning of each scene that pro- vide information such location and time of day (Cole and Haag, 2002). This standardized for- mat facilitates parsing and segmentation of movie scripts into individual scenes. In the case of film scripts, a paragraph usually corresponds to a single
We formulate baseline algo- rithms which uses greedy matching, where the pairs of paragraphs are aligned decreasing or-
Greedy Baselines:

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der of their similarity values while maintaining a 1-1 book-script match. Both SBERT+SW and SBERT+greedy use SBERT embeddings as the sim- ilarity metric, but SW can utilize the similarity information of the neighborhood paragraphs dur- ing aligmment, which the greedy baseline cannot. Therefore any improvement of SW+SBERT over baseline must be attributed to the SW alignment method.
the text gets a penalty for aligning with an early movie scene and vice versa. This technique aims to restrict alignments between distant scenes and chapters. However this assumption becomes trou- blesome when the two sequences do not follow similar order e.g., the seventh HP book and the two movies adapted from it. Our method does not assume global prior, allowing it to success- fully align both the movies with the correct parts of the book, as shown in Figure 1. Book2Movie correctly aligns the whole first movie with the first part of the book, while our method aligns 97.5% of the first movie correctly. But Mook2Movie aligns only 41.4% of the second film correctly, whereas our method achieves 81.6% accuracy there. This proves how global priors can negatively influence accuracy of alignment algorithms.
5.2 Retention Accuracy
Book retention is a weaker standard than fine align- ment. We manually annotated each book unit as retained or removed in the movie for the six book- script pairs in Table A.1. In this context, a false pos- itive book unit denotes that the algorithm marked this unit as retained wrongly, while true positive book unit denotes that both the algorithm and the manual annotator marked this unit as retained. We use 6,546 book units in this experiment.
Analysis of the Film Adaptation Process
The economics of the movie industry imposes stronger constraints on the length of scripts than the publishing industry forces on books. Our align- ments illustrate the relationship between book and script lengths, and running times. Figure (left) shows that the retention percentage of a book in the movie has an inverse relationship with the length of the book, whereas Figure 2 (middle) shows that longer movies tend to retain more parts of the books. Finally, Figure 2 (right) shows that longer books tend to have longer movie adaptations. Script retention information for all book-movie pairs in our dataset is presented in the Figure 3, using alignments constructed by our SW+SBERT method.
Our results are presented in Table 2. The SBERT and
combination produced the best Fl score for retention classification for five of the six book-script pairs. On the remaining instance it finished second to SBERT with the greedy heuris- tic. Based on these results, we will use SBERT+SW on our film adaptation analysis to follow.
5.3 Alignment Accuracy
Building gold-standard alignments between book- length novels and scripts is a challenging task. We manually aligned movie scenes with book chapters for two book-script pairs (HP2 and HP4) and compared our SW+SBERT method against Book2Movie (Tapaswi et al., 2015) and Greedy+SBERT baselines. Accuracy is measured as the percentage of correctly aligned scenes.
The book-script alignment methods we have devel- oped so far enable us to analyze the film adapta- tion process in several interesting ways. The sub- sections below will consider four particular tasks, namely: (i) measuring the faithfulness of film adaptation to its original source, (ii) the influence of dialog on script retention, (iii) the preservation of temporal sequencing in adaptation, and (iv) gen- der representation decisions in film adaptation.
The Book2Movie method formulates the alignment problem shottest-path finding task in graph, employing Dijkstra's algorithm to solve it. They utilize dialogue and character frequency as their similarity metric and directly align chapters with scenes. We created a simple dialog and character frequency based similarity metric to fit into the Book2Movie alignment algorithm. Our method's s alignment accuracy is 25.8% and 13.7% more than MovkeMovie for HP2 and HP4 respectively, pre- sented in the Table 1.
6.1 Faithfulness of Movies to Source Novels
The faithfulness of movie adaptations to their orig- inal source material has long been a topic of re- search and discussion (Leitch, 2008). Our align- ment methods provide quantify the faithfulness of scripts to their source books.
The Book2Movie algorithm assumes global pri- ors, so that a book chapter towards the end of
We use the alignments for 40 book-movie pairs

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120
1000
knggt (n papes)
reterton peronttige
26
8
16
400
30
202
20
0
200
400
300
200
1000
1202
160
100
120
540
100
180
200
kingth of book (n pagus)
length movie
lingth of modie minutee))
Figure 2: Relationships between book length, script length, and playing time. Scripts contain smaller fractions of longer books: correlation -0.58 (on left). Scripts for longer movies contain larger fractions of book: correlation 0.09 (in middle). Longer books lead to longer movies: correlation 0.36 (on right).
We have annotated 18 movie adaptations with bi- nary labels indicating faithfulness based on critic reviews. We describe the labelling process and the data sources in Appendix E. The retention percentage demonstrates Spearman rank correla- tion of 0.65 with a p-value of 0.001 and achieves an AUC score of 0.875 for classifying faithful- ness. This strengthens the cause for using retention percentages found by SW+SBERT as faithfulness metric.
High aedieeery
seraiied
6.2 Retention of Dialog from Books
Figure 3: Alignments for 40 book-movic scripts. Here the parts of the book are colored based on if they are not retained or retained, with low or high confidence, by SW+SBERT according the generated alignment scores.
Dialogs between characters is a critical aspect of visual media. We hypothesize that movie adapta- tions tend to retain the more hiavy parts of the book than descriptive parts without speech.
To quantify this preference, we compute a dialog retention ratio u, for each book- pair b as:
presented in the Figure 3 to identify which text in each book gets aligned, marking it as retained in the script. We propose using the percentage of retained text a metric for measuring script faithfulness, acknowledging that films completely focused on a small section of the book may be classified as unfaithful.
rb del (de)
[b]
Wb
(3)
rb
where тか denotes the retained part of book b, and de denotes the part of book b with dialogs. Similarly we compute Ub. the representative value for retain- ing texts without dialogs. A value greater than for u, signifies that dialog-heavy parts retained more than non-dialog parts. Figure shows that for majority of the books, texts with dialog are retained more than texts without it.
Table presents the books with the highest and lowest amount of retention respectively. The sec- ond Jurassic Park film (The Lost World), based on the novel by Michael Crichton with the same name, had the lowest retention in our dataset, which "an incredi-
supports claims that the movie bly loose adaptation of Crichton's second book" (Weiss, 2023). On the other end of the spectrum, both crities (Blohm, 2016) and our alignments finds two movic adaptations based on Jane Austen's nov- els to be the most faithful to its source material.
Statistically, find that dialog has mean retention of u 1.04, compared non-dialog with a mean retention of 0 0.97. A I-test comparing the dis- tribution of un and ta over all books in our data set shows that they follow two different distributions
with a p-value < 3 x 10 6. The effect size calcu-

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1.0
Fraction ND retained in the movie
Namber of pa parpgraghe (hg roalls
0.8
0.6
0.4
0.2
0.0
22
0.0
0.2
0.4
0.6
0.8
1.0
Fraction of D retained in the movie
Figure 5: Books and scripts generally proceed in the same temporal order, so book-script alignments should increase monotonically. We plot the length of the longest increasing subsequence (LIS) of each n-part alignment blue, and compare the expected LIS (in red) for a random permutation (2/n). The black points denote the upper limit. The significantly higher LIS lengths prove that local Smith- Waterman alignments capture the global temporal order.
Figure 4: Paragraphs of books with dialog (D) are more likely to be retained in the script than paragraphs con- taining no dialog (ND), Each blue point denotes book- script pair, represented by the fraction of dialog and no-dialog text retained in the script. The majority of the pairs (33/40) fall below the red diagonal x = y line.
lated to be 1.09 using Cohen's 's d, signifying large effect.
6.4 A Modified Bechdel Test
The cultural importance of movies makes the unbi- ased representation of women in film an important issue. The Bechdel test (Bechdel, 1986) provides a simple framework for evaluating the representation of women in films. To pass the test, a film must sat- isfy three requirements: (i) There are two women in the film (ii) who talk with each other (iii) about something other than men. Previous works have focused on its usage for analysing films and society as a whole (Selisker, 2015) as well as automating the test (Agarwal et al., 2015). However, its binary nature fails to capture the degree of unbiased rep- resentation. Our ability to segment books into the respective portions which are/are not included in corresponding movie script permits to study the issues of adaptation through Bechdel-inspired lens along a continuum.
6.3 Sequential Orderings in Books and Adaptations
Narrative order in both books and films generally follow sequentially with time, despite the existence of literary devices such as flashbacks. We pro- pose that the sequential order of events in books and scripts will largely coincide. Such hypoth- esis cannot be tested with global alignment al- gorithm such Book2Movie, because such global alignments are forced to be sequential by design. But our local Smith- Waterman alignments permit meaningful test, by quantifying the degree of mono- tonicity exhibited
book-script alignment. SW aligns each book paragraph to an arbitrary para- graph in the script or let it remain unaligned.
To score aligment monotonicity, we compute the longest increasing subsequence (LIS) of the aligned book-script pair, representing the longest story por- tion in the book presented in the same order in the script. Figure 5 presents the length of LIS for all book-movic pairs in dataset, along with the expected length according to chance. Logan and Shepp (1977) proved that the expected LIS of two random n-element permutations ~ 2/n. All our alignments display much larger increasing subse- quences, closer to the trivial upper bound of n.
Let d represent the set of all dialogs in a book, dy denote the dialogs of female characters without any mention of male characters, and rd encompass all retained dialogs in the film. We introduce the Bechdel representation ratio B the ratio between retention fraction of dy and overall retention frac- tion of dialogs.
|dg.6 Ard
[d]
B [dy.
(4)
rd

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Year (Book. Movie)
Reten- tion
metrics quantify similarity, yielding better align- ments using the Waatt-naatemmnn algorithm. This study can shed light onto how much the auditory and visual analysis strengthens the adaptation anal- ysis process.
Book Name
Critic Opinion
Lost
1995,
"incredibly adapuationd" (Weiss, 2023)
13.8
World
1997
last finunaker you want adopt your novel to the screen if lonking for foithful retelling "(Davis, 2018)
1965.
Dune
1984
14.1
The Grapes
of Wrath
1940
1989
18.9
Limitations
Anna
1878,
Karenina
2012
The Removed Parts Annotation Set discussed in Section 4 employs a non-standard segmentation unit called book units. While chapters and para- graphs are standard alternatives, they each face their own challenges. Chapters are too large to be annotated as fully removed or retained in film adap- tation while annotating paragraphs will require high number of human hours. Book units presented a promising alternative that required less resources and also created units more likely to be completely removed or retained in the film. However the man- ual annotator occasionally thought a book unit was partially retained in the film. For future research, annotating book paragraphs would be an alternative given sufficient resources, although our annotated book unit dataset remains a readily available option for evaluation.
All the
1916.
"vury wilddly from another" (Davis, 2018)
26.4
King's Men
1949
The Two
1954,
"Surprizingly Book Accurate" (Gass, 2019)
Towers
2002
79.8
The Return
1955,
"Surprizingly Book Accurate" (Gass, 2019)
81.2
of King
2003
Harry
1998,
"Jolimwed the book the mont closety" (Collier, 2017)
Potter
2002
84.3
Pride and
1813,
"Jairly fankffll to the source" (Blihh, 2016)
89.4
Prejudice
2005
1816,
"adapted fairyy juibifally to film" [Bishm, 2016)
Emma
91.7
1996
Table 3: Faithfulness of screen adaptation: the five film scripts with the least/most book retention in our dataset.
We consider books with a Bechdel representation ratio B= > 1 to be a good predictor of whether the film is likely to pass the Bechdel test. For collection of 26 book-film pairs which have been assessed on the Bechdel test by crowdsourcing (Hickey, 2014), only of the 10 films pass with b 5 1, while 10 of 16 pass with b > 1, i.e. our al- gorithm correctly identifies the Bechdel test result for 17/26 film adaptations. More details about the data can be found in Appendix F.
The alignment of books with their film adaptations is inherently subjective. Different human annota- tors are likely to have disagreements in alignment construction. With ample resources, employing multiple human annotators and calculating inter- annotator agreement using Cohen's 's Kappa would provide more accurate assessment. The inclusion of a third annotator resolve conflicts would fur- ther enhance the quality of annotations and provide insights into the performance of different methods. However, due to limited resources, we did not pur- sue multiple annotations in this study.
7 Conclusion
In this paper, we have adapted a local dynamic pro- gramming alignment algorithm to efficiently align books and scripts and conducted a mroof-of-concep analysis of 40 film adaptations, revealing insights into screenwriters' decision decision-making from various perspectives. Our work demonstrates that align- ment methods can be used to identify which parts of books appear in movies, and opens up new re- search directions regarding how films are adapted from books. Future research directions include exploring patterns in screenwriting process that re- flect the prevailing zeitgeist and cultural dynamics. By leveraging automated methods, it is possible to investigate how these patterns vary across different time periods, cultures as well as for characters of different genders and ethnicities.
Many of the books and film scripts cannot be dis- tributed in its original form due to copyright issues. This creates a hurdle for future researchers. To ad- dress this, upon acceptance of this paper, we will share the code and a step-by-step replication strat- egy for reproducing the of the dataset. This will enable researchers with access to the book and movie script to reproduce the dataset and utilize our annotations.
portion
We employ SBERT embeddings as the similarity metric, which have been trained on a dataset with texts of similar length to our task. Exploring the impact of training a specialized SBERT model on book and movie movie-specific data could provide further
One future study would be create model that can embed video frames and book text in the same embedding space essentially converting two differ- ent media to the same space that can create better

---

insights. Computationally more expensive models such as SGPT (Muennighoff, 2022) may provide more accurate similarity measure.
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Appendix
the book, while also altering the text substan- tially. Despite this deviation from the original source material, our algorithm adeptly cap- tures the fundamental core of the narrative, wherein a character laments their unequivo- cal failure to catch the train, and successfully aligns the pertinent textual components.
A Manually Annotated Dataset
The six books that were manually annotated listed in Table A.1. Two graduate students anno- tated the books. The only requirement for the an- notators was that they had read and watched their respective book and film. They did not have any additional expertise related to literature or films. They were given the book units in a spreadsheet where they annotated the units with binary labels. In case of book units which they thought were par- tially retained, they were instructed beforehand to annotate it as retained if they thought the majority of the content was present in the film. Annotating a book took on average ~ 4 hours.
D Different Similarity Metries
For a comparative analysis of SW+SBERT, we use 4 other similarity metries both for our baseline greedy and SW alignment method. The metrics are described below:
Jaccard: The Jaccard index treats text bag- of-words and computes the similarity using the multiset of words present in both text segments:
The HP2 film and book had I 16 scenes and 18 chap- ters respectively. On the other hand, the HP4 film and book had 68 scenes and 37 chapters. Anno- tating alignment of chapter-scenes for book-film pair took similar amount of time on average as retention annotation for a book.
aNb b(a a ubb
(5)
The Term Frequency Inverse Document Frequency (TF-IDF) measures text similarity using the frequency of words weighted by the inverse of their presence in the texts, giving more weight to rare words. Here the of documents is repre- sented by the concatenated set of paragraphs from the book and film script.
TF-IDF:
B Complete Dataset
Information about all 40 book-films are listed in Table B.1. Each book-film pair given unique ID. The books are ordered lexicographically in the table using their IDs. Information about lengths of the books and the films are additionally provided in Table B.2.
Glo Ve Mean Embedding: We represent a text by the average of Glo embeddings of all the words in the text, using cosine similarity.
Ilustrative Example Alignment
Hamming Distance: Given two paragraphs con- taining n 2 m words, we decompose the longer paragraph into chunks of size n/m. The Ham- ming distance h(.,,) is the fraction of chunks where chunk does not contain word from the shorter text. We then use
In Table C.1 and Table C.2, we show the alignment of a scene from the second Harry Potter film and the film (e05) with their respective original source novels. It is to be noted that the complete script and book had been aligned for both of them and we extract the particular scene's alignment to illustrate the following observations:
-(.,,)) as the similarity score.
E Faithfulness Labels for Film Adaptations
In Table C. Script paragraph 429 is left un- aligned correctly, showing the necessity of gapped alignments and the gap penalties as discussed in Section 3.1.
From different online reviews by critics, we com- piled faithfulness labels for 18 film adaptations based on how faithful they were to their original novel. We only assigned labels when critic review unambiguously described the film as either faith- ful unfaithful. For example, for the film Anna Karenina (2012), a review describes the film as faithful but then goes on writing
Paragraph 430 and 431 are aligned with the same book paragraph, proving the necessity of many-to-many alignment instead of 1-1 align- ment, also discussed in Section 3.1.
hhttps://nlp.ttaree www.:/forr..uuuooooccsss///////o htttss/:/ceetteeeeenntteeeeorere /01/21/
In paragraph 438, the screenwriter assigns Harry the lines originally attributed to Ron in

---

Film
No. of Book. Units
Human Annotation Retention
ID
Book (Year)
Author
Film Adaptation (Year)
Length (Minutes)
Harry Potter and
Harry Potter and The Chamber of Secrets
HP2
Rowling
The Chamber
161
759
59.5
Secrets (2002)
Harry Potter and
Harry Potter and The Goblet of Fire
HP4
K. Rowling
The Goblet
157
1400
27.6
of Fire (2005)
Harry Potter and
Harry Potter and The Deathly Hallows
HP7P1
K. Rowling
The Deathly
146
1605
40.3
Hallows Part (2010)
Harry Potter and
Harry Potter and The Deathly Hallows
HP7P2
Rowling
The Deathly
130
1605
24.7
Hallows Part (2011)
Pride and
PP
Pride and Prejudice
Jane Austen
127
498
48.6
Prejudice (2005)
Princess
PB
The Princess Bride
William Goldman
98
677
55.8
Bride (1987)
Table A.I: Data the six book-script pairs whose books units manually annotated removed retained. Additionally we create manual alignment of HP2 and HP4 book chapters with their movie scenes.
"The book is just short of 350,000 words so many choices had be made."
discussed in Section 6.4 are presented in Table F.I.
"effectively trims a significant portion of the book"
"leaves out entire characters."
"stays clear of Tolstoy's long discussions of Russian contemporary issues and the inner spiritual reflections of the characters."
For such reviews, we opted not to create a label and denote by the ? symbol in Table E.I.
F
Data for Bechdel Test
We combine three different sources to get the Bechdel test result for films in our dataset.
For the film, Dr. Jekyll and Mr. Hyde (1941) we get the Bechdel test result from https: //h2g2.com/ edi
1112g2.com/ediiee__entttet /eee/ttteeeeettt//777555...
For the films The Wonderful Wizard of Oz (2011) and # (1970), we use data from https://lisashha..c
Hickey (2014) compiled a dataser using the data from
test. where Bechdel test results of films are stored by users. We intersected our dataset with theirs to get the result for the other 23 films.
The Bechdel test results, Bechdel, female and male representation ratios computed by our algorithm as
'https: : thu.. com/ fivettiirtyeigh///att/bboo. /naster/bechdell htw.eeechhheeeiieeeeeeee csv

---

Animation
Genre
Sci
Romance
Fantasy
Thriller
Sei-f
Romance
Romance
Romance
Fantasy
Horror
Thriler
Thriller
Droma
Horror
Sci-h
Horror
Comedy
Fantasy
Fantasy
Fantasy
Fantasy
Sci-B
Drama
Horror
Sci-fi
Action
Drama
Thrille
Comedy
Romance
Romance
Fantasy
Thriller
Romance
Thrillr
Sci-h
Thmller
Fantasy
Fantasy
Film Year
1982
2012
2003
2004
1984
1996
1996
2015
2001
1994
1972
1971
1940
1987
20015
1999
1970
2002
2005
2010
201
1999
1941
1949
1997
1997
1993
1955
1988
1983
2004
2005
2003
2015
2008
1990
1960
2009
2002
1939
Harry Potter and the Chamber of Secrets
The Hitchhiker & Grüde to the Gialaxy
Harry Potter the Deuthly Hallows
Deathly Hallows
I Know What You Did Last Summer
LOTR: The Fellowship of the Ring
Harry Potter and the Goblet of Fire
The Lost World: Jurassic Park
LOTR: The Return of the King
Par from the Madding Crowd
Mary Shelley s Frankenstein
Table B.I: Complete datasel of 40 book and film adaptations with their IDs listed.
The Phantom of the Opera
The Bourne Supremacy
Dr. Jekyll and Mr. Hyde
The Night of the Hunter
LOTR: The Two Towers
The English Patient
The Grapes Wrath
All the King's 's Men
The Three Musketeers
The Princess Bride
Pride and Pre judice
Revo lutioont Road
The Time Machine
The Wizard of Oz
Blade Runner
Annn Karenina
The Getaway
The Haunting
Harry Poller and
The Iron Giant
The Grifters
Film Name
Big Fish
Get Carter
Hellraiser
M M'A"S S
Die Hard
The Road
Dune
Emma
Room
Robert Louis Sievenson
Robert Penn Warren
Lyman Frank Baum
Michael Ondeatje
Michael Crichton
Alexandre Dumas
William Goldman
Emma Donoghue
Comma McC McCarily Carthy
Philip K Dick
Leo Tolstoy
Daniel Wallace
Robert Ludlum
Frank Herhert
Jane Austen
Thomas Hardy
J. R. R. Tolkien
Mary Shelley
Jim Thompson
Ted Lewis
John Steinbeck
Clive Barker
Douglas Adamss
Shirley Jack son
Richard Honker
JK Rowling
JK Rowling
JK Rowling
JK Rowling
Tod Hug hes
Lois Duncan
Davis Grubb
Roderick Thorp
Gaston Leroox
Jane Austen
1955 J.R. R. R. Tolkien
Richard Yates
Jim Thonpson
H. Wells
J. R. R. Tolkien
Book Year
1968
1878
1998
1986
196.5
1885
1992
1874
1954
18 8818
1958
1970
1939
1986
1979
1959
1968
1998
2000
20007
2007
1968
1886
1946
1973
1995
1844
1953
1979
1973
1910
181.3
2010
1961
1963
1895
20006
1954
1900
MASH: A Novel About Three Army Doctors
Frankensteing or, The Modern Prometheus
Big Fish: A Novel of Mythic Proportions
Harry Potter and the Chamber of Secrets
Do Androids Dream of Electric Sheep
Harry Pottr and the Deathly Hallows
Deathly Hallows
Strange Case of Dr Jekyl and Mr Hyde
Know What You Did Last Summer
Get Carter - Jack's Return Home
Harry Potter and the Goblet of Fire
Hitchiiker's & Guide to the Gialaxy
Far From The Madding Crowd
The Wonderful Wizard of Oz
The Fellowship of the Ring
The Haunting of Hill House
The Phantom of the Opera
The Bourne Supremacyy
The Night of the Hunter
The Return of the King
The Grapes Wrath
The Hellbound Heart
The Three Musketeers
Nothing Lasts Forever
The English Patient
All the King's s Men
The Princess Bride
Pride and Prejudice
Revolutionary Road
The Time Machine
Anna Karenina
Harry Potter and
The Lost World
The Two Towers
Book Name
The Gelaway
The Iron Man
The Grifters
The Road
Dane
Emma
Room
HPTP1
HP7P2
ID
AD
AK
BF
BS
DU
EM
EP
FE
FR
FS
GA
GC
GW
HB
HG
HH
HO
HP2
HP4
IM
JH
KM
LS
LW
MK
NH
NL
PB
PO
PP
RK
RO
RR
TG
TM
TR
TT
WW

---

Table B.2: Length information all book-films. A page estimated to words.
Book Length
Script Length
Film Length
No. of Script Paragraphs
No. of Book
Refen-
(In Pages)
(In Pages)
(In Minutes)
Paragraphs
tion %
AD
243
79
117
1630
1818
61.3
AK
1264
130
2126
7402
22.1
152
86
125
1926
1063
71.6
851
6784
27.3
DU
768
80
155
1316
7842
14.1
EM
575
49
124
1601
2285
91.7
EP
299
103
162
1725
1611
73.3
FF
496
72
119
2696
3471
68.7
FR
685
80
137
1708
4141
43.1
261
187
122
1809
1403
65.4
GC
265
52
112
1340
3442
38.7
GW
762
86
129
1464
5884
18.9
HB
110
66
93
1784
1107
69.8
HI
175
81
110
1923
2023
56
HH
261
114
1798
1642
73
228
2255
47.4
HP2
316
94
161
2278
3116
84.3
HP4
706
44
157
2780
5994
34.8
1PP7P1
726
102
146
2196
6778
46.9
HP7P2
726
105
130
2377
6778
33.8
IM
36
62
87
1438
279
70.2
JH
145
98
3998
2090
72.2
KM
809
4616
26.4
LS
198
39
101
848
1644
40.2
LW
467
108
129
2719
14456
13.8
MK
837
93
105
3378
8132
29.3
NH
267
80
92
2217
2483
68
234
88
1887
1567
74.9
332
98
2028
2876
304
2371
60.6
PP
443
85
127
1662
2065
89.4
RK
493
100
201
2344
2492
81.2
RO
361
75
118
2273
5036
31
RR
384
57
119
1436
1614
64
TG
182
75
110
1556
1650
63.4
TM
96
2021
$1.1
214
1403
1957
62.3
TT
570
128
179
3834
2876
79.8
WW
204
108
101
2061
1665
72.9

---

Table C.I: Alignment of the scene where Harry and Ron misses the train to Hogwarts in HP2.
Script
Script Paragraph
Aligned Book Paragraph
Paragraph
#
MRS. WEASLEY
take Ginny and you two come right after Mrs Weasley told Harry and Ron, grabbing Ginny's hand setting the blink an eye they were gone.
(O.S.)
Come, Ginny. We'll get you seat.
429
32B INT. KING'S CROSS 32B
430
Ron glances at the clock.
Let's go together. we've only got minute, Ron said Harry. Let's go together, we' 've only got minute, Ron said to Harry.
431 RON
We better hurry.
432 Harry nods, leans into his trolley and CRASH! hits the barrier and bounces back into Ron. GUARD glowers.
Harry made that Hedwig's cage safely wedged on top of his trunk and wheeled his trolley about face the barrier. He felt perfectly confident; this wasn't 't nearly uncomfortable using Floo powder. Both of them bent low the handles of their trolleys and walked purposefully towards the barrier, gathering speed A few feet away from it, they broke into and CRASH. ASH.
433
GUARD
Both trolleys the barrier and bounced backwards. Ron's trunk fell off with loud thump, Harry was knocked off his feet, and Hedwig's 's cage bounced onto the shiny floor and she rolled away, shricking indignantly. People all around them stared and guard nearby yelled. What in blazes d' you think you re doing?
What blazes
d' y'uou two think you're re doing?
434
HARRY
Lost control of the trolley Harry gasped, clutching his rihs he got up Ron ran to pick up Hedwig, who causing such eruelty to animals from the surrounding crowd.
Sorry. Lost con- tral of the trolley.
that there lot of muttering about
435
(to Ron) Why can't we get through?
Why can't we get through?' Harry hissed to Ron.
436
RON
We're going to miss the train Ron whispered don't understand why the gateway's sealed itself Harry looked up at the giant clock with sickening feeling in the pit of his stomach.
dunno. The gateway's 's scaled itself for some reason.
437 Ron presses ear bar- rier, the CLOCK CHIMES.
Ten seconds... seconds... wheeled his trolley forward cautiously until was right against the barrier, pushed with his might.
438 HARRY
Three seconds...two seconds...one second ...' It's gone, said Ron, sounding stunned.
The train leaves at exactly eleven o' c'clo... We've missed it.
439
RON
The Dursleys haven't given me pocket money for ahout years. Ron pressed his the cold barrier.
Can't hear thing.
440
(a sudden thought) Harry. If we can't get through, maybe Mum and Dad can't get back.
What re we going to do? don't know how long it'l take Mum and Dad to get back to us They looked around.
441
HARRY Maybe should go wait by the car.
think we'd 'd better go and wait by the car, : said Harry. We're attracting much atten Harry!
442
RON
said Ron, his eyes gleaming Hogwarts!
The car!" What about it?' We can fly the car to
The car!

---

Table C.2: Aligning the scene depicting Jack feigning death in front of his captor, Old Nick, in the film Room (2015) with its source book.
Script Para- graph
Script Paragraph
Book Paragraph
833
The dnor heeps, and Ma rolls Jack up, very fast. We're in the dark with him, the rug pressing close.
Beep boop.
834 Sound of Old Nick stepping in. Proud of himself.
Here you go " Thha's Old Nick's voice sounds like always. He doesn't even know what's happened about dying. Antihiotics, only just past the sell by For you break them half, guy said. Ma doesn't answer. Here you go. " "That's Nick's voice sounds like always doesn't even know what's ' happened about me dying. Antibioties, only just past the sell by doesn't answer.
835
OLD NICK (O.S.) Antihiotics.
you them half, guy Ma
836
The door shuts with boom.
837
OLD NICK (O.S.) (CONT'D)
Are you nuts, wrapping sick kid
up in that?
838
Ma's voice is very close because she's bent over the rug.
Is he in the rug?
839
MA (O.S.) He got in the night.
He worse in the night and this morning he wouldn't it wake up Nothing.
840
She waits, giving time to figure out.
Then Old Nick makes a funny sound. Are you sure?"
841 In the rug, Jack lies frozen except for his flickering eyes.
Am sure? Ma shrieks it, but don't move, don't move, I'm all stiff no hearing sceing no nothing.

---

ID
Movie
Faithfulness Source
Faithful
Retention %
The Lost World:
https//wwwwssmmlleme http://wwwwmmmooooooooooooooaaaa com//movii-allapaatonns
13.8
Jurassic Park (1997)
that-were-nohhnn tike-the-bookk
hups://www.sii simplemost. http://mmos...oooooooooeoo.oo coom/movie-/iie
ttpp://www.ttuplemsss.
con//movee-dlaplations
DU
Dune (1984)
mott-wee-nothing tike-the-booke
x
14.1
Anna Karenina (2012)
wtwp://eeeeeeeeettwwwrerrss.... wordpress.com/ 2013/01/21/
https//creativveccettiiccc
AK
?
22.1
All the King's
www.simplemot..oom/meet
KM
Men (1949)
that sott-were-ool weerennthhing like- the--hooee
x
26.4
The Bourne Supremacy (2004)
https:/wwww cettppeemos..cou/ooiiieeeeeeee
BS
27.3
that-were- sereeeoootinn-iieeeeeeee like-the-book/
https:// http://wwwwwwww..co///// 1993/1
1/12/mvvies/
The Three Musketeers (1993)
MK
x
29.3
athos- titos-porthos-eee -tt-ioss--t---atttt
Harry Potter
http: https///creemrant.ccom http://erraantcooaaaaaaaeeee htthhhhaaary-----mmaeee potter-most-
HP7P2
Part 2 (2011)
accurate- movies based-books/ books/
Harry Potter 4 (2005)
https//screemmant.com/ www/hamy-pootteeeeeeeooee
HP4
?
34.8
accurate-movics base-- based-books
Know What You Did
https://www. wwtp:/wwwwww.reeeeeee.eeeeo com/
LS
Last Summer (1997)
978081313658 ofttteepoooooooooooooooo
x
40.2
LOTR: The Followship
https://crcenrratt www.:/iiiss/mmm/meee//ooo/ooeee surriisiiyr
FR
v
43.1
of Ring (2001)
book aakk-accrrate/tth-looddd -orrttteee-oo-----loe-ee-------- of-d-oo-the-ring the-rings
Harry Potter
https://screemrant.com http://aranntccaaaraaaaaeae potthrrr----tt---osttt
HP7P1
?
46.9
Part (2010) Blade Runner (1982)
accurate-movies basee--bbeeeedeebe---------- based-books
http://www.simplemmsst. http:///msst.oooooooooaaaaaaaaaa
61.3
The Grifters (1990)
TG
/
Mary Shelley's Frankenstein (1994)
https:/eolliddrrccmmmaaax--ba/b
FS
/
69.1
frankenstein adaptation/
http://www.indiwwi.
The Iron
IM
5-things-you-miiee www.mm--ooo-oo---abbeeeeeee
Giant (1999)
birds-the- thrss-th-iroon- -aa---roonnng------ee 10ant-1075111
The Wizard (1939)
https:/collider.com//eee www.//ooo----ilm--aaaa-----eeeee film-adaptaaiions-
WW
not-like- bookbo
x
72.9
NL
Die Hard (1988)
https///ollidee..commmwwwvv nothing like-book
x
74.9
LOTR: The Two
TT
/
Towers (2002)
book-accura
LOTR: The Return of the King (2003)
htpss//sreemaaaa.ccoommvveeesssp book-accurate/
RK
4
81.2
Harry Potter 2 (2002)
https://www. theodysseyonline -ooi/sss
IIP2
1
84.3
haall-oo-bookk--ht------------ actually-liikkk wikullllll-keeeeee the-book
Pride and Prejudice (2005)
http:://ntcrestinlltruum com/2016/03/jim
PP
V
89.4
austen addataaionsssn throughout-historyy
https//interestinliteriit com//006//0//jnnc
FM
Emma (1996)
91.7
austen adaptaatonssss thtttttoootoosssttieee
Table E.1: Faithfulness of 18 film adaptations have 0.65 Spearman correlation with retention percentage computed by SW+SBERT. For four film adaptations, the reviews were not deemed to be convincing enough to label the films faithful unfaithful, denoted the ? symbol the Faithful column.

---

Table F.I: A book/script with Bechdel ratio I is good predictor of the offical Bechdel test, correctly predicting 17/26 films.
SW+SBERT
Female Represen- tation Ratio
Male Representa- tion Ratio
Bechdel Representa- tion Ratio
Bechdel Test
Prediction
ID
Book/Film Name
Correct-
HO
Mash
0.767
L.052
0.711
Pass
PB
The Princess Bride
0.803
1.036
0.85
Fail
HP7P2
Harry Potter Part
0.822
1.065
0.852
Fail
BF
Big Fish
0.93
1.015
0.913
Fail
KM
All the King's Men
0.951
1.037
0.95
Fail
NL
Nothing Lasts Forever
0.963
1.023
0.902
Fail
Harry Potter
0.963
1.011
Fail
Fellowship the Ring
0.998
0.992
Fail
PP
Pride and Prejudice
0.998
Pass
EM
Emma
1,001
0,998
1.001
Pass
RR
Revolutionary Road
1,011
0.993
1.003
Pass
LS
Know What You Did Last Summer
1,013
0.973
1.076
Pass
WW
The Wonderful Wizard Oz
1.017
0.969
1,011
Pass
HIII
Haunting Hill House
1.017
0.967
Pass
AK
Anna Karenina
1.02
0.987
1.026
Fail
RK
The Return of the King
10035
0.997
1.03
Fail
FS
Frankenstein
1.035
0,984
1.034
Fail
FF
Far From The Madding Crowd
1.048
0.967
1.045
Pass
HB
The Hellbound Heart
1.064
0.925
1.048
Pass
TG
Grifters
1.08
0.952
Pass
AD
Androids Dream Electric Sheep
L,084
0.974
Fail
GC
Get Carter (Jack's Return Home)
1.116
0,952
1.078
Fail
PO
The Phantom of the Opera
1.116
0.953
1.113
Fail
HPTP1
Harry Potter Part
1.117
0.957
1.068
Pass
JH
Dr. Jekyll and Mr. Hyde
1.14
0,994
1.13
Pass


Reviews:
[{"review_id": "Oq7ffjg07P", "reviewer": "Reviewer_MAg3", "summary": "", "questions": "A)\tGiven the limitations of a text-centric approach, have you explored how incorporating visual or auditory analysis to capture essential cinematic elements could enhance the quality of interpreting book-film adaptations?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your detailed review.\n\nWe agree that visual and auditory analysis can enhance the quality of book-to-film alignment, and script analysis in general. We explored how the text-to-text alignment methods can capture how screenwriters are adapting the book writers' books into film scripts. Our current method draws a parallel between book writers and screenwriters. But we agree that a film goes through more modifications as the director, editor, actors and others bring in their own ideas which can only be captured through audio+video analysis. One future study would be to create a model that can embed video frames and book text in the same embedding space essentially converting two different media to the same space. Then we could use embedding similarity to quantify our similarity metric and use it with the Smith-Waterman alignment algorithm. Embedding video frames and related book text in the same space is an interesting research problem itself.  We will propose such a project in the future work section of our final version of this paper.\n\nNevertheless, the first step in the book-to-film adaptation process is always the screenwriter creating the script, which arguably contains the biggest decisions to be made in the adaptation process. We successfully showed how alignment between the book and the script alone can reveal significant insights into the adaptation process. A future work should definitely dive deeper into how much the auditory and visual analysis strengthen the adaptation analysis process.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This paper presents a compelling approach to the analysis of book-film adaptation, introducing an alignment method that demonstrates superior performance compared to robust baselines. The thorough evaluation of this algorithm and the incisive investigation into adaptation patterns yield valuable insights that enrich both scholarly and practical perspectives within the realms of screenwriting and adaptation. The paper stands to offer the NLP community an improved methodology and curated datasets for deeper understanding and exploration of the complex interplay between textual sources and cinematic adaptations.", "reasons_to_reject": "The paper offers a valuable approach to analyzing book-film adaptation but has some weaknesses to consider. It primarily focuses on textual analysis, potentially overlooking important cinematic elements like aesthetics, sound, and performance. This limitation could restrict the comprehensive understanding of the adaptation process and potentially curtail the depth of insights attainable. Moreover, the heavy reliance on textual similarity measures may oversimplify the intricate process of film adaptation, posing a risk of misconstrued adaptation quality. Such simplification might disregard the intricate interplay of elements like narrative tempo, emotional subtleties, and creative choices that profoundly influence the quality of film adaptations.", "questions_to_authors": "A)\tGiven the limitations of a text-centric approach, have you explored how incorporating visual or auditory analysis to capture essential cinematic elements could enhance the quality of interpreting book-film adaptations?", "paper_topic_and_main_contributions": "The paper analyzes the alignment of literary works with their corresponding film adaptation. An alignment technique based on Smith-Waterman's sequence alignment algorithm and NLP techniques for computing text similarity is introduced. The paper further introduces three manually annotated datasets to enhance research endeavors within this domain. The paper's main contributions include a deep evaluation of the proposed alignment method, insights into adaptation patterns such as retention of book parts, dialog representation, sequential orderings, and gender representation. The paper's findings have practical implications in advancing computationally aided linguistic analysis and understanding of the adaptation process.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "At line 325 and 825, the phrase \"In case\" should be corrected to \"In the case\".\nAt line 379, the typographical error \"unitsin\" should be corrected to read as \"units in\".\n"}, "extra_text": {}}, {"review_id": "CALHQ220Yx", "reviewer": "Reviewer_ybnJ", "summary": "", "questions": "- If I understood correctly, the text segmentation relies on \"book units\" for the text of the book, and \"scenes\" for the text of the script.  What is the typical length of a scene? Have you experimented with other segmentation choices for the script than scenes?\n- did you investigate whether the relative position of a book unit in a book had an influence on the retention probability? For instance, it would be interesting to see whether paragraphs occurring in the first chapter (often providing some general introduction to the plot) were less likely to be retained.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your detailed review.\n\nWe agree that the question being investigated has historically been niche, but with the advent of GPT-3 and more advanced models, this has coincidentally come into limelight. AI systems are increasingly being proposed to automate creative tasks in the entertainment industry. The Writers Guild of America has been on strike since May regarding multiple issues, one of them being “Regulate use of artificial intelligence on MBAcovered projects: AI can’t write or rewrite literary material; can’t be used as source material; and MBA-covered material can’t be used to train AI” [from “WGA Negotiations—Status as of May 1, 2023”]. We believe our quantitative analysis tools will make the screenwriting adaptation process an increasingly important area of study. Potential application of our tool for both sides of the debate may include: \n1. Creating training dataset for generative AI for rewriting creative materials into different formats.\n2. Analyzing the differences between materials created by humans and AI, as well as identifying areas where AI may have limitations.\n\nWe agree that we should refer to the alignment methods developed for statistical (Lopez, 2008) and neural machine translation such as  Bahdanau et al., 2014 and discuss how the lengths of texts in our dataset make it challenging to use them out-of-box due to resource scarcity and memory requirements. We would also add discussion on how monolingual parallel corpus are created using alignment algorithms (Paun, 2021). Upon acceptance of the paper,  we would also add the missing references related to movie scripts in the camera-ready version and we thank the reviewer for pointing them out. \n\n\nOur book unit segmentation and scene segmentation makes the dataset annotation and evaluation process simpler, more intuitive and more interpretable. The typical length of a scene is ~28 paragraphs where each script paragraph represents either a setup of the scene or a single dialog. We did not experiment with other segmentation choices for scripts, because scenes seemed to be the most intuitive option.\n\nWe did perform some preliminary experiments where we divided the book into three parts: setup (first 25%), confrontation (next 50%), and resolution (last 25%).  But we did not find any significant correlation between the three parts with the retention probability over our dataset of 40 book-movie pairs. But it is possible that some smaller specific parts of the books exhibit a pattern of being removed or retained majority of the time. Figure 3 does show us that the portions at the very beginning of books tend to be disproportionately removed, which somewhat supports the reviewer's hypothesis. We will further investigate whether the relative position of the book units have any correlation with the probability of retention, and report it in the camera ready version of the paper.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- the paper is clear and well-written\n- the experimental design is generally sound\n- the paper investigates an interesting question and provides interesting empirical data on film adaptation", "reasons_to_reject": "- the question being investigated is rather niche, and might not be of interest for a broad audience.\n- the discussion of related work does not mention at any point the large body of work on alignment algorithms developed for machine translation (to align sentences or paragraphs in parallel or comparable corpora).", "questions_to_authors": "- If I understood correctly, the text segmentation relies on \"book units\" for the text of the book, and \"scenes\" for the text of the script.  What is the typical length of a scene? Have you experimented with other segmentation choices for the script than scenes?\n- did you investigate whether the relative position of a book unit in a book had an influence on the retention probability? For instance, it would be interesting to see whether paragraphs occurring in the first chapter (often providing some general introduction to the plot) were less likely to be retained.", "paper_topic_and_main_contributions": "The paper presents an approach to automatically analyse film adaptation of books by running a local alignment algorithm between the book and the corresponding movie script. The approach is tested on 40 adaptations and relies on the Smith-Waterman algorithm together with similarity measures based on sentence-BERT. Those alignments are then used to investigate various aspects of the film adaptation, such as the faithfulness of the adaptation or the preservation of the narrative order.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "- the acronyms \"PB\" and \"PP\" are only explained in the appendix."}, "extra_text": {"Missing_References": "There has also been some work on aligning movie scripts with subtitles, see Lison, P., & Meena, R. (2016, December). Automatic turn segmentation for movie & tv subtitles. In 2016 IEEE Spoken Language Technology Workshop (SLT) (pp. 245-252). IEEE."}}, {"review_id": "kr4PokuBwi", "reviewer": "Reviewer_CXES", "summary": "", "questions": "What motivated your choices re: algorithms and models? e.g. why SBERT vs SGPT? why GloVE vs ELMo?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your detailed review.  We will incorporate your new references into the final version of our paper.\n\nOur primary technical goal in this paper was to test how similarity metrics from different domains (such as sentence embedding based, mean word embedding based, and traditionally popular metrics such as Jaccard, TF-IDF) performed for our particular use-case of quantifying similarity between book paragraphs and script paragraphs. We mostly opted to use the lightweight options unless the lightweight option proved much weaker compared to its alternative. The definition of \"much\" here is subjective. \n\nWe preferred SBERT over SGPT for the following reasons:\n1. The reported performance of SGPT using a similar number of parameters as SBERT is typically similar or poorer in most datasets—SGPT only became better when the number of parameters became multiple magnitudes higher. But this also creates larger computational resource requirements, which are challenging in our research environment.\n2. SBERT has a better/simpler API to use and has been more widely used in the research community than SGPT, admittedly because it got published four years earlier.\n3. As we discuss in Section 3.3, SBERT trained using MS-Marco dataset seemed to align with our use-case with regards to the lengths of texts we compute the similarity metric for, one short text against a longer text.  \n\nNevertheless, we agree with the reviewer that an analysis between SBERT and SGPT will be interesting for our experiments. We will try to do a subset of our experiments using two SGPT models, one model with similar number parameters as SBERT and the other being the largest SGPT model and add a comparative analysis in the camera-ready version upon acceptance of the paper. \n\nThe mean word pooling method we used helped more in setting a baseline performance level as opposed to increasing the performance. Elmo usually uses a higher embedding dimension compared to GloVe. We opted for the lightweight GloVe here for resource demands.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The study of adaptations is an underexplored and underresourced task in narrative NLP. This paper could kickstart more work in this space, particularly due to its datasets and its detailed experimental comparisons for alignment and faithulness.\n\nIt seems the authors were in a tough spot between ethics (respecting copyright) and reproducibility (providing the dataset). Their planned solution to provide a step-by-step replication strategy seems to be a reasonable compromise.", "reasons_to_reject": "The chosen techniques used for alignment (e.g. Smith-Waterman+ SBERT) and faithfulness (GloVE, Hamming distance etc) may be somewhat dated and it's possible that newer / more tailored techniques might perform better (e.g. newer or finetuned sentence representations; contextualized embeddings; algorithms used in the detection of paraphrases or plagiarism).*\n\nThe provided datasets are somewhat small, especially gold alignments, and may be insufficient to judge the effectiveness of the chosen techniques. However, it is understandable that copyright protections and limited resources posed constraints.\n\n\\* EDIT: However, as the authors explained, they deliberately gave preference to techniques that would not be very computationally expensive.", "questions_to_authors": "What motivated your choices re: algorithms and models? e.g. why SBERT vs SGPT? why GloVE vs ELMo?", "paper_topic_and_main_contributions": "This paper offers an analysis of adaptations of novels into films. It provides three datasets, two of them with manual annotations. It tests techniques to align novels and films that is an improvement compared to previous work (finding that local alignment with SBERT performs best). It also explores ways to measure faithfulness to the source material. Last, they provide a measure w.r.t. the extent of retention of dialogs that pass the Bechdel Test in each adaptation.", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "Scene reordering in movie script alignment. (Lambert et al., 2013) is an older relevant work. Stephen Follows' blog contains useful studies of screenplay vs film runtime.", "Typos_Grammar_Style_and_Presentation_Improvements": "Suggestion for the code - it would be good to include package versions in your requirements file."}, "extra_text": {}}]

Author rebuttal:

Meta-review: There is a perfect agreement among reviewers about the soundness and excitement of this paper. Some reasons to reject do not have to do with soundness or excitement about the paper (research question is too niche, neglect of cinematic elements, literature on alignment, …), but may be explored in future work.

Paper Topic And Main Contributions:

This paper introduces a novel approach to analyzing the alignment between literary works and their film adaptations by employing the Smith-Waterman sequence alignment algorithm and sentence-BERT-based similarity measures. It presents three datasets, including two with manual annotations, and evaluates the alignment technique across 40 adaptations. The study delves into various aspects of the adaptation process, shedding light on patterns like narrative faithfulness, retention of book elements, dialog representation, and gender portrayal. These findings hold significant practical implications for advancing computational linguistic analysis and enhancing our understanding of the adaptation process, making it a valuable contribution to the field.

Reasons To Accept:

* Novelty
    * The paper addresses an underexplored area in narrative NLP, potentially catalyzing more research in this domain due to its valuable datasets and comprehensive experimental comparisons for alignment and faithfulness.
    * The inclusion of curated datasets enriches the research community's resources for further exploration of the intricate relationship between textual sources and cinematic adaptations.
    * The proposed alignment method demonstrates superior performance compared to established baselines, indicating the paper's potential to advance the field of NLP.
* Quality of the Methodology
    * The paper investigates a compelling question regarding book-film adaptation and offers interesting empirical data, contributing valuable insights to both scholarly and practical discussions in screenwriting and adaptation studies.
    * The authors have navigated the ethical challenge of copyright while ensuring reproducibility by providing a well-considered step-by-step replication strategy.
    * The experimental design is robust, enhancing the credibility of the presented results.
* Quality of the writing
    * The paper is well-written and clear, making it accessible to a broad audience.

Reasons To Reject:

* The heavy reliance on textual similarity measures oversimplifies the complex film adaptation process, potentially leading to misconstrued assessments of adaptation quality, as it doesn't consider factors like narrative tempo, emotional subtleties, and creative choices that significantly influence film adaptations.
* The chosen techniques for alignment and faithfulness assessment (e.g., Smith-Waterman, SBERT, GloVE, Hamming distance) may be outdated, and more recent or tailored methods might yield better results. This suggests a potential need for a more up-to-date approach to ensure the paper's findings remain relevant.
* The provided datasets, especially the gold alignments, are relatively small, which may limit the assessment of the effectiveness of the chosen techniques. While copyright limitations and resource constraints are acknowledged, larger datasets could provide more robust and reliable results.
Decision: Accept-Main