| { | |
| "paper_id": "2020", | |
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| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T07:29:01.291048Z" | |
| }, | |
| "title": "Generating Intelligible Plumitifs Descriptions: Use Case Application with Ethical Considerations", | |
| "authors": [ | |
| { | |
| "first": "David", | |
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| "last": "Beauchemin", | |
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| "institution": "Universit\u00e9 Laval", | |
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| "region": "Qu\u00e9bec", | |
| "country": "Canada" | |
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| }, | |
| "email": "david.beauchemin.5@ulaval.ca" | |
| }, | |
| { | |
| "first": "Nicolas", | |
| "middle": [], | |
| "last": "Garneau", | |
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| "institution": "Universit\u00e9 Laval", | |
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| "email": "nicolas.garneau@ift.ulaval.ca" | |
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| "email": "eve.gaumond@observatoire-ia.ulaval.ca" | |
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| "abstract": "Plumitifs (dockets) were initially a tool for law clerks. Nowadays, they are used as summaries presenting all the steps of a judicial case. Information concerning parties' identity, jurisdiction in charge of administering the case, and some information relating to the nature and the course of the preceding are available through plumitifs. They are publicly accessible but barely understandable; they are written using abbreviations and referring to provisions from the Criminal Code of Canada, which makes them hard to reason about. In this paper, we propose a simple yet efficient multi-source language generation architecture that leverages both the plumitif and the Criminal Code's content to generate intelligible plumitifs descriptions. It goes without saying that ethical considerations rise with these sensitive documents made readable and available at scale, legitimate concerns that we address in this paper. This is, to the best of our knowledge, the first application of plumitifs descriptions generation made available for French speakers along with an ethical discussion about the topic.", | |
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| "text": "Plumitifs (dockets) were initially a tool for law clerks. Nowadays, they are used as summaries presenting all the steps of a judicial case. Information concerning parties' identity, jurisdiction in charge of administering the case, and some information relating to the nature and the course of the preceding are available through plumitifs. They are publicly accessible but barely understandable; they are written using abbreviations and referring to provisions from the Criminal Code of Canada, which makes them hard to reason about. In this paper, we propose a simple yet efficient multi-source language generation architecture that leverages both the plumitif and the Criminal Code's content to generate intelligible plumitifs descriptions. It goes without saying that ethical considerations rise with these sensitive documents made readable and available at scale, legitimate concerns that we address in this paper. This is, to the best of our knowledge, the first application of plumitifs descriptions generation made available for French speakers along with an ethical discussion about the topic.", | |
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| "section": "Abstract", | |
| "sec_num": null | |
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| "text": "The right to access judicial information is a fundamental component of Canadian democracy and its judicial process (Vancouver Sun (Re), 2004; CBC. v Canada (A.G), 2011) 1 . This right has two main purposes. First, to enhance judicial accountability by providing opportunities to the public to scrutinize it and put forward criticisms of the judicial process (Sierra Club of Canada v Canada (Minister of Finance), 2002; CBC. v New Brunswick (A.G), [1996] ). Second, it has an educational purpose: by accessing judicial information, people acquire a better understanding of the court process (Edmonton Journal v Alberta (A.G), [1989] ). Given these purposes, the necessity to provide access to judicial information in an intelligible form cannot be ignored. Indeed, getting a copy of a document is not enough; people have to understand its contents. This is particularly crucial in a digital context since citizens face an overload of judicial information online (Eltis, 2011) . As a consequence, litigants have great difficulty in finding relevant information for their case online (Dionne, 2019) .", | |
| "cite_spans": [ | |
| { | |
| "start": 447, | |
| "end": 453, | |
| "text": "[1996]", | |
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| { | |
| "start": 625, | |
| "end": 631, | |
| "text": "[1989]", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 961, | |
| "end": 974, | |
| "text": "(Eltis, 2011)", | |
| "ref_id": "BIBREF7" | |
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| { | |
| "start": 1081, | |
| "end": 1095, | |
| "text": "(Dionne, 2019)", | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| "text": "Studies show that, in the province of Quebec, the plumitif (a public register where one can find an official trace of all the actions taken by the courts) lacks intelligibility (Tep et al., 2019) . Some users have called it \"non-sense\" for non-attorneys (Parada et al., 2020 ). Yet, the plumitif is necessary for every litigant as it provides information concerning the parties' identity, the jurisdiction responsible for administering cases, and information relating to the nature and the course of proceedings. In this work, we aim at leveraging both information extraction and natural language generation to increase the intelligibility of excerpts of the Court of Quebec's plumitif regarding criminal offenses under the Criminal Code of Canada (CCC) .", | |
| "cite_spans": [ | |
| { | |
| "start": 177, | |
| "end": 195, | |
| "text": "(Tep et al., 2019)", | |
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| { | |
| "start": 254, | |
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| "text": "(Parada et al., 2020", | |
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| { | |
| "start": 741, | |
| "end": 753, | |
| "text": "Canada (CCC)", | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| "text": "Improving the comprehension of textual legal documents has been the subject of several studies in the past. For example, patent claims are long legal pieces of texts that contain complex sentences making it hard for a layperson to reason about. Sheremetyeva (2014) framed this problem into an automatic text simplification task while Farzindar et al. (2004) and Hachey and Grover (2006) proposed extractive summarization techniques to make them easier to understand. The plumitifs, while also lying in the \"legal texts\" family, take a completely different form; they are not written in a valid grammatical form, and contain many abbreviations and references to the CCC. This makes our use case application rather unique.", | |
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| { | |
| "start": 245, | |
| "end": 264, | |
| "text": "Sheremetyeva (2014)", | |
| "ref_id": "BIBREF25" | |
| }, | |
| { | |
| "start": 334, | |
| "end": 357, | |
| "text": "Farzindar et al. (2004)", | |
| "ref_id": "BIBREF8" | |
| }, | |
| { | |
| "start": 362, | |
| "end": 386, | |
| "text": "Hachey and Grover (2006)", | |
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| "section": "Introduction", | |
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| "text": "To handle this type of document, we have de-signed a description generation pipeline, divided into three steps. The first step consists of segmenting a plumitif into different parts. In the second step, we extract, for each part, the relevant information using a Named Entity Recognition (NER) model. For the final step, we generate sentences from the data extracted by the NER model. To this end, we use a template-filling approach to ensure there are no factual fallacies introduced in the generation, an essential concern in legal text generation. Moreover, we use a statistical language model in a controlled setting to augment the generation with vital contextual information, namely texts from the CCC, making our approach a hybrid generation model. Our contributions, in this work, are twofold; 1. We propose a simple yet robust data-to-text multi-source textual generation pipeline to make plumitifs easier to understand for the litigants (made available through a web application, see Appendix I); 2. We bring a discussion on the ethical considerations about privacy and discrimination that such an application may cause. We further describe our architecture, related work and methodology in Section 2 and evaluate its generation capabilities in Section 3. We bring important ethical considerations in Section 4 and open the discussion for future work in Section 5.", | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| "text": "Plumitifs are used as summaries presenting all the steps of a case heard by the court. In the context of criminal proceedings, they contain information about the plaintiff, the accused, different charges along with their associated penalty (if applicable). We present a plumitif example in Appendix A, Figure 2. Plumitifs are freely available in person at any courthouse and are also accessible on the Soci\u00e9t\u00e9 qu\u00e9b\u00e9coise d'information juridique (SOQUIJ) website 2 where they can be consulted for a fee. In this section, we detail our proposed architecture, which is broken down into three steps; segmenting the plumitif into parts, extracting relevant information from each part, and generating descriptions by also leveraging the CCC. We illustrate the whole architecture in Appendix B, Figure 4 and further detail each component in the following subsections.", | |
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| "section": "Generating Intelligible Plumitifs Summaries", | |
| "sec_num": "2" | |
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| { | |
| "text": "2 https://soquij.qc.ca/", | |
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| "section": "Generating Intelligible Plumitifs Summaries", | |
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| "text": "We identify three parts in a plumitif ; the accused, the plaintiff, and the charges. Since the plumitif structure is pretty regular, it allows us to identify each one using simple heuristics based on the presence of specific strings (e.g. \"ACC.\" for \"accused\") with 100% accuracy. Splitting into parts simplifies the NER step since these models typically use a narrow contextual window of a few tokens on either side to make their prediction. It also provides more data points overall.", | |
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| "section": "Segmenting the Plumitif", | |
| "sec_num": "2.1" | |
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| "text": "As mentioned in Section 1, we frame the retrieval of the relevant entities in the plumitif as an information extraction problem. That is, given a raw part of the plumitif, a NER model extracts entities from the text to fill in a normalized view. We established nine types of entities that need to be extracted; Adresses (Addresses), Accusations et sp\u00e9cifications d'accusations (Charges and Charges Specifications), Dates, D\u00e9cisions (Decisions), Lois (Laws), Accusations, Organisations (Organizations), Personnes (Persons), Plaidoyer (Pleas) and Peines (Sentences). For the rest of the paper, we will use the French entities within the French templates and rules, and the English entities otherwise (i.e. in the text). We manually annotated 816 plumitifs from eight districts over the last five years, to cover as much variety as possible. These eight districts are the ones with the most cases for this date range. We train a NER model on the annotated dataset, which achieves, on average, a F1-Score of 0.965, thanks to the regularity in the form the plumitifs can take 3 .", | |
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| "section": "Extracting Relevant Information", | |
| "sec_num": "2.2" | |
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| "text": "Once the relevant information is extracted and normalized, we use it in the third step of the pipeline, which consists of a data-to-text generation model, described in the following subsection.", | |
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| "text": "Even though statistical and deep Natural Language Generation (NLG) has seen tremendous breakthroughs in recent years (Radford et al., 2018 Brown et al., 2020) , we decide not to strictly rely on this kind of Transformer model (Vaswani et al., 2017) for our use case. Several architectures (Ziegler et al., 2019; Keskar et al., 2019; Dathathri et al., 2020) attempt to control the generation of such pre-trained models by using conditioning elements that propose a specific stylistic or emotion for example. However, Brown et al. (2020) showed that one of the best neural language models to date (GPT-3) may generate non-factual utterances, often called hallucinations (Rohrbach et al., 2018; Rebuffel et al., 2020) , or even hide significant biases that may put the credibility of generation at stake.", | |
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| "section": "Realisation of Plumitif Summaries", | |
| "sec_num": "2.3" | |
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| "text": "Since we generate legal textual content that can be used in various sensitive applications (e.g. HR screening, (Parada et al., 2020 )), we can't afford to let a model \"statistically\" generate a non-factual decision (e.g. guilty but the accused is not) or a charge (e.g. something that the accused has not done). Thus, we prefer to sacrifice variability for control by using a template-filling approach. Puzikov and Gurevych (2018) showed that a template-based approach can be as good as a neural encoder-decoder model on generating restaurant descriptions from sets of key-value pairs. Deemter et al. 2005also argues that \"template-based approaches to the generation of language are not necessarily inferior to other [statistical] approaches as regards their maintainability, linguistic well-foundedness and quality of output\". This approach has been shown recently to perform well in different areas like weather reports (Ramos-Soto et al., 2015), financial analysis (Nesterenko, 2016) and soccer game reports (van der Lee et al., 2017) where they are used in production.", | |
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| "text": "In the same way Deemter et al. (2005) did, we manually deduce 66 patterns from a subset of the plumitifs to generate the description text using the extracted information from the model introduced in Section 2.2 4 . The generation rules (especially the sentence ones) have been written by a legal expert. Following the example in Figure 2 , with the corresponding extracted information about the accused and a really simple yet efficient rule, we can generate texts about the accused and the plaintiff, as illustrated in Appendix D.", | |
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| "section": "Template-Based, Data-to-Text Generation", | |
| "sec_num": "2.3.1" | |
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| "text": "In the next subsection, we present how we combine the information extracted from the plumitif with a parsed version of the CCC 5 using a Masked Language Model.", | |
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| "section": "Template-Based, Data-to-Text Generation", | |
| "sec_num": "2.3.1" | |
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| "text": "The Criminal Code of Canada (CCC) is an act that contains most of the criminal law in Canada. It contains around 1,500 provisions (referred to with numbers) where each of them comprises paragraphs and subparagraphs. The plumitifs refers to provisions from the law using only the provision numbers, which provides little to no context to the litigants. Therefore, it is essential to extract the law's text from the Criminal Code when generating the plumitif 's summary. However, the CCC is only available in HTML or PDF format, making it hard to query it programmatically. Thus, we parsed the HTML version into the JSON format, which allows us to easily query for different articles, paragraphs and subparagraphs 6 . A plumitif may contain several charges. Each charge may refer to one or two provisions from the law. The first provision is most likely referring to the description of the law, where the title briefly summarizes the description. The second provision (if any) is usually there to specify the charge 7 .", | |
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| "section": "Leveraging the Criminal Code of Canada", | |
| "sec_num": "2.3.2" | |
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| "text": "Given the following template (see Appendix G for a translated version); <Accus\u00e9> est accus\u00e9 <Article>.", | |
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| "text": "we wish to insert the provision title syntactically. To this end, we propose to \"stitch\" the two pieces of the template using a Masked Language Model. We use the French pre-trained version of BERT (Devlin et al., 2019) , CamemBERT (Martin et al., 2020) , which has been trained on the French subset of OSCAR (Ortiz Su\u00e1rez et al., 2020), a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus.", | |
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| "section": "Leveraging the Criminal Code of Canada", | |
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| "text": "One of BERT's abilities is to predict randomly masked tokens in a sentence, usually referred to as a Cloze task in the literature (Taylor, 1953) . We specifically leverage this ability to our benefit, and let CamemBERT predict the proper preposition that should be inserted between the template and the charge's title (d\u00e9faut de se conformer\u00e0 une ordonnance here). The realisation of the previous template would then look like the following (Appendix G); John Doe est accus\u00e9 pour d\u00e9faut de se conformer\u00e0 une ordonnance.", | |
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| "sec_num": "2.3.2" | |
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| "text": "Using the 134 unique charges titles included in our dataset, we find that CamemBERT can predict the right preposition 84% of the time.", | |
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| "section": "Leveraging the Criminal Code of Canada", | |
| "sec_num": "2.3.2" | |
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| "text": "The generation of the pleas and decision text is simple since there are only a few possible situations, using 14 generation rules out of the 66 deduced. For the first, it is either guilty or not guilty. For the second, it is guilty, not guilty, or ten other technical situations such as \"arret\" (i.e. case where the court orders a stay of proceedings). In both cases, the mapping between the pleas and decision is one-toone with the associated generated text (i.e. a guilty decision can generate only one text). We illustrate this case in Appendix E.", | |
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| "section": "Pleas, Decisions and Sentences", | |
| "sec_num": "2.3.3" | |
| }, | |
| { | |
| "text": "On the other hand, generating Sentences is more complex. In our set of 66 deduced generation rules, 50 are used to generate the Sentences. This complexity is mostly due to the occurrences of different convictions in one Sentence, meaning that the mapping is one-to-many (i.e. a Sentence can have an unknown number of convictions). Given the Sentence's extracted convictions, we order them by types (i.e. the penalty inflicted of, fines and fees, community work, other convictions, probation and surcharge) and fill-in an \"on-the-fly merged generation template\" given the list of convictions. It is important to note that generation rules are not applied \"in cascade\" i.e. for a given list of convictions, there is one possible generation template. We illustrate the generation of the first Sentence's section in Appendix F.", | |
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| "section": "Pleas, Decisions and Sentences", | |
| "sec_num": "2.3.3" | |
| }, | |
| { | |
| "text": "Since our generation model mostly relies on rules, it is straightforward to evaluate its performance; we first need to make sure all the relevant information is fully extracted (NER step) and that it properly fills in the corresponding template (generation step).", | |
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| "section": "Evaluating the Realisation of the Summaries", | |
| "sec_num": "3" | |
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| "text": "We thus quantify our model's performance in terms of \"Error Rate\" where a generation error is the lack of realizing a specific part (accused, plaintiff or list of charges paragraphs), instead of evaluating the textual generation. The counts are computed per text. Errors are split into two categories; Extractionbased Errors (EE) and Generation-based Errors (GE). For clarity, we display the Errors Rates by districts in Table 1 . In most cases, we find that a wrong extraction of the Plaintiff (due to the NER model) causes EE.", | |
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| "start": 421, | |
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| "ref_id": "TABREF1" | |
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| "text": "We can see that Granby and Sherbrooke have the highest EE rate; this is mostly due to the many different values an Organisation can take in these districts 8 . GE are mainly due to edge cases found in plumitifs which our rules do not cover. As we can see from the GE Rates in Table 1 , our generation rules commit most errors on the Montr\u00e9al, Sherbrooke and Gatineau districts. This is due to the numerous and diverse convictions these plumitifs hold. For example, a particular combination of convictions may not be associated to any generation rule. We illustrate this problem with an example in Figure 1 , where the Sentence comprises multiple convictions and are essentially edge cases about the duration. This highlights the need to have a better model at parsing and generating Sentences' paragraphs. Using a generative, sequence-to-sequence model, such as the one proposed by (Bahdanau et al., 2015) may be a better option, but we leave this study as future work. All in all, our model achieves low Error Rates (13% EE and 5% GE on average), allowing simple yet accurate textual generation of intelligible plumitifs. While these results are interesting, it raises some ethical concerns, that we discuss in the next section.", | |
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| "start": 882, | |
| "end": 905, | |
| "text": "(Bahdanau et al., 2015)", | |
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| "text": "Figure 1", | |
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| "sec_num": "3" | |
| }, | |
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| "text": "There is some ethical considerations regarding our dataset's privacy that ought to be addressed. Plumitifs contain sensitive information such as the names, dates of birth, addresses and criminal backgrounds of accused people. The identity of judges, plaintiffs, clerks, and attorneys taking part in a criminal case are also found in the plumitifs. As explained in Section 1, all of this information must be publicly accessible. As long as this data is protected by practical obscurity 9 , the actual risks from public access of this information are limited (Vermeys, 2016) .", | |
| "cite_spans": [ | |
| { | |
| "start": 557, | |
| "end": 572, | |
| "text": "(Vermeys, 2016)", | |
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| "sec_num": "4" | |
| }, | |
| { | |
| "text": "However, if this data was to be released in bulk to the scientific community, it would not be \"scattered [. . .] bits of information\" (US Department of Justice v. Reporters Committee for Freedom of the Press, 1989) that require time and resources to retrieve anymore. Information could be easily searched, aggregated or combined with information from other public sources. This poses a risk to the privacy of judicial stakeholders.", | |
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| "sec_num": "4" | |
| }, | |
| { | |
| "text": "In this subsection, we explain why we decided not to release our data set publicly (raw or synthesized). To put it in straightforward terms: information collected in public records should not be \"up for grabs\". Its use can result in privacy violations. This is especially true in the digital context where aggregation, linkage and analytics are made easier (Martin and Nissenbaum, 2017) . There are several examples of privacy violations that occurred due to the malicious use of judicial information that was publicly accessible. For instance, more than 270 cases of identity theft have been linked to a security lapse in an American Municipal Court's website. (Bailey and Burkell, 2017) . The Office of the Privacy Commissioner of Canada had to intervene to end an extortion scheme relying on data available from the Canadian Legal Information Institute and SOQUIJ's websites (A.T. v Globe24h.com, 2017). United State's \"Public Access to Court Electronic Records\" system made the identity of some cooperating defendants and undercover agents publicly available, which contributed to the intimidation and harassment of witnesses in order to discourage them from testifying (Eltis, 2011). There have also been some docu-9 A term broadly used to explain that documents might be accessible to all in principle, but that the access is hindered by some obstacles such as fees to consult a document or the need to go physically to a location -as is the case for the plumitif.", | |
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| "start": 357, | |
| "end": 386, | |
| "text": "(Martin and Nissenbaum, 2017)", | |
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| }, | |
| { | |
| "start": 662, | |
| "end": 688, | |
| "text": "(Bailey and Burkell, 2017)", | |
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| }, | |
| { | |
| "text": "mented cases of discrimination in the context of employment (Solove, 2002) and housing (Gichuru v Purewal and another, 2017) caused by judicial information available online. Moreover, academics have expressed significant concerns about the secondary use of judicial information for marketing purposes.", | |
| "cite_spans": [ | |
| { | |
| "start": 60, | |
| "end": 74, | |
| "text": "(Solove, 2002)", | |
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| }, | |
| { | |
| "text": "This is now prohibited by the Personal Information Protection and Electronic Documents Act, (Office of the Privacy Commissionner of Canada, 2014) , but (Bailey and Burkell, 2017) argues that this regulatory framework is not sufficient to prevent inappropriate uses of judicial data. Our team is currently working to develop a framework for the management of personal information contained in digital court records. However, for the moment, since the law provides no satisfactory solution, we chose not to release the dataset used to train our algorithm.", | |
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| "start": 132, | |
| "end": 145, | |
| "text": "Canada, 2014)", | |
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| "start": 152, | |
| "end": 178, | |
| "text": "(Bailey and Burkell, 2017)", | |
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| "sec_num": "4" | |
| }, | |
| { | |
| "text": "In this paper, we introduce a simple yet effective multi-source architecture able to generate digestible plumitifs for Canadian citizens. We also show that we are in a position to easily divulge who has been accused of what and the outcome of it, which raises some important ethical concerns. In the future, we plan to explore statistical natural language generation further by using case law, provide more diverse plumitifs descriptions and improve the generation of Sentences. Finally, we hope that our application will provide better insights to the community and give the right direction for the next applications of not only NLG, but Machine Learning in general, in the field of law.", | |
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| "section": "Conclusion and Future Work", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "Training details are available in Appendix C", | |
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| "text": "We present a complete generation example in Appendix H based on the plumitif presented inFigure 2.5 https://laws-lois.justice.gc.ca/eng/ acts/c-46/", | |
| "cite_spans": [], | |
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| "text": "We were able to properly extract the 1518 provisions publicly release the JSON version of the French CCC here: https://bit.ly/3kiBdFd7 In this work, we do not leverage the second provision.", | |
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| "text": "This corroborates with the results of the NER model for the entity Organisation, in Section 2.2", | |
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| "text": "We thank the reviewers for their insightful comments on our manuscript. This research was enabled in part by the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Social Sciences and Humanities Research Council (SSHR). Also, this research was made possible with the help of our partner, SOQUIJ.", | |
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| "section": "Acknowledgements", | |
| "sec_num": "6" | |
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