Using genre-specific features for patent summaries

Codina-Filb, Joan; Bouayad-Agha, Nadjet; Burga, Alicia; Casamayor, Gerard; Mille, Simon; Mller, Andreas; Saggion, Horacio; Wanner, Leo

doi:10.1016/j.ipm.2016.07.002

Cited by 18 publications

(7 citation statements)

References 26 publications

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“…Moreover, many words are used unusually: said, for example, typically refers back to a previously mentioned entity, repeated to minimize ambiguity (e.g., A system for [...], said system comprising [...], in claim 1); transitions (e.g., comprising, including, wherein, consisting) have specific legal meanings. The Claim's language is abstract (system, object, medium in claim 1), not to limit the invention's scope, while the Description is more concrete (Codina-Filbà et al, 2017).…”

Section: Patent Languagementioning

confidence: 99%

Summarization, Simplification, and Generation: The Case of Patents

Casola,

Lavelli

2021

Preprint

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We survey Natural Language Processing (NLP) approaches to summarizing, simplifying, and generating patents' text. While solving these tasks has important practical applications -given patents' centrality in the R&D processpatents' idiosyncrasies open peculiar challenges to the current NLP state of the art. This survey aims at a) describing patents' characteristics and the questions they raise to the current NLP systems, b) critically presenting previous work and its evolution, and c) drawing attention to directions of research in which further work is needed. To the best of our knowledge, this is the first survey of generative approaches in the patent domain.

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Section: Patent Languagementioning

confidence: 99%

Summarization, Simplification, and Generation: The Case of Patents

Casola,

Lavelli

2021

Preprint

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“…There exist different categories for document summarization. For instance, one is based on the goal of the summarization task, which includes generic, domain-based (topic-focused) [10], or query-based summarization algorithm [34]. We also have other categories for document summarization which is based on the application of summarization such as article summarization [35], review summarization [18], news summarization [36], and also summarization for anomaly detection [1].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Summaries: A Personalized Concept-based Summarization Approach by Learning from Users' Feedback

Ghodratnama¹,

Zakershahrak²,

Sobhanmanesh³

2020

Preprint

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Exploring the tremendous amount of data efficiently to make a decision, similar to answering a complicated question, is challenging with many real-world application scenarios. In this context, automatic summarization has substantial importance as it will provide the foundation for big data analytic. Traditional summarization approaches optimize the system to produce a short static summary that fits all users that do not consider the subjectivity aspect of summarization, i.e., what is deemed valuable for different users, making these approaches impractical in real-world use cases. This paper proposes an interactive concept-based summarization model, called Adaptive Summaries, that helps users make their desired summary instead of producing a single inflexible summary. The system learns from users' provided information gradually while interacting with the system by giving feedback in an iterative loop. Users can choose either reject or accept action for selecting a concept being included in the summary with the importance of that concept from users' perspectives and confidence level of their feedback. The proposed approach can guarantee interactive speed to keep the user engaged in the process. Furthermore, it eliminates the need for reference summaries, which is a challenging issue for summarization tasks. Evaluations show that Adaptive Summaries helps users make high-quality summaries based on their preferences by maximizing the user-desired content in the generated summaries.

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“…Tseng et al [32] presented an automatic method for patent documents based on text summarisation. Similarly, Codina-Filba et al [33] employed genre-specific feature sets for patent document summarisation.…”

Section: Literature Reviewmentioning

confidence: 99%

An ensemble scheme based on language function analysis and feature engineering for text genre classification

Onan

2016

Journal of Information Science

258

119

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Text genre classification is the process of identifying functional characteristics of text documents. The immense quantity of text documents available on the web can be properly filtered, organised and retrieved with the use of text genre classification, which may have potential use on several other tasks of natural language processing and information retrieval. Genre may refer to several aspects of text documents, such as function and purpose. The language function analysis (LFA) concentrates on single aspect of genres and it aims to classify text documents into three abstract classes, such as expressive, appellative and informative. Text genre classification is typically performed by supervised machine learning algorithms. The extraction of an efficient feature set to represent text documents is an essential task for building a robust classification scheme with high predictive performance. In addition, ensemble learning, which combines the outputs of individual classifiers to obtain a robust classification scheme, is a promising research field in machine learning research. In this regard, this article presents an extensive comparative analysis of different feature engineering schemes (such as features used in authorship attribution, linguistic features, character n-grams, part of speech n-grams and the frequency of the most discriminative words) and five different base learners (Naïve Bayes, support vector machines, logistic regression, k-nearest neighbour and Random Forest) in conjunction with ensemble learning methods (such as Boosting, Bagging and Random Subspace). Based on the empirical analysis, an ensemble classification scheme is presented, which integrates Random Subspace ensemble of Random Forest with four types of features (features used in authorship attribution, character n-grams, part of speech n-grams and the frequency of the most discriminative words). For LFA corpus, the highest average predictive performance obtained by the proposed scheme is 94.43%.

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Using genre-specific features for patent summaries

Cited by 18 publications

References 26 publications

Summarization, Simplification, and Generation: The Case of Patents

Summarization, Simplification, and Generation: The Case of Patents

Adaptive Summaries: A Personalized Concept-based Summarization Approach by Learning from Users' Feedback

An ensemble scheme based on language function analysis and feature engineering for text genre classification

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