# Evaluation of Diversification Techniques for Legal Information Retrieval +
## 4 Related Work
### Legal Text Retrieval  
With respect to legal text retrieval that traditionally relies on external knowledge sources, such as thesauri and classification schemes, various techniques are presented in [36]. Several supervised learning methods have been proposed to classify sources of law according to legal concepts [37; 38; 39]. Ontologies and thesauri have been employed to facilitate information retrieval [40; 41; 42; 43] or to enable the interchange of knowledge between existing legal knowledge systems [44]. Legal document summarization [45; 46; 47] has been used as a way to make the content of the legal documents, notably cases, more easily accessible. We also utilize state of the art summarization algorithms, but under a different objective: we aim to maximize the diversity of the result set for a given query. Finally, a similar approach with our work is described in [48], where the authors utilize information retrieval approaches to determine which sections within a bill tend to be outliers. However, our work differs in the sense that we maximize the diversify of the result set, rather than detect section outliers within a specific bill. In another line of work, citation analysis has been used in the field of law to construct case law citation networks [49]. Case documents usually cite previous cases, which in turn may have cited other cases, and thus, a network is formed over time with these citations between cases. Case law citation networks contain valuable information, capable of measuring legal authority [50], identifying authoritative precedent (the legal norm inherited from English common law that encourages judges to follow precedent by letting the past decision stand) [51], evaluating the relevance of court decisions [52] or even assisting with summarizing legal cases [53], thus showing the effectiveness of citation analysis in the case law domain. While the American legal system has been the one that has undergone the widest series of studies in this direction, recently, various researchers applied network analysis in the civil law domain, as well. The authors of [54] propose a network-based approach to model the law. Network analysis techniques were also employed in [55], demonstrating an online toolkit allowing legal scholars to apply network analysis and visual techniques to the entire corpus of EU case law. In this work, we also utilize citation analysis techniques and construct the legislation network, as to cover a wide range of possible aspects of a query. This paper is an extended version of our paper [2], where we originally introduced the concept of diversifying legal information retrieval results with the goal of evaluating the potential of results diversification in the field of legal information retrieval. We adopted various methods from the literature that where introduced for search result diversification (MMR [3], Max-sum [4], Max-min [4] and Mono-objective [4]) and evaluated the performance of the above methods on a legal corpus objectively annotated with relevance judgments. In our work [56], we address result diversification in the legal IR. To this end, we analyze the impact of various features in computing the query-document relevance and document-document similarity scores and introduce legal domain-specific diversification criteria. In this work, extending our previous work presented in [2], we extended the methods utilized by incorporating methods introduced for text summarization (LexRank [5] and Biased LexRank [6]) and graph-based ranking (DivRank [7] and Grasshopper [8]) in parallel with the search result diversification methods utilized in our previous work (MMR, Max-sum, Max-min and Mono-objective). We adopted the text summarization and graph-based ranking methods in our diversification schema, and additionally, we utilized various features of our legal dataset. In detail, text summarization methods (LexRank and Biased LexRank) were originally proposed for computing the relative importance of textual units within a document for assisting summarization tasks.