Towards automatic context-aware summarization of code entities

Software developers are working with different methods and classes and in order to understand those that perplex them and–or that are part of their tasks, they need to tackle with a huge amount of information. Therefore, providing developers with high-quality summaries of code entities can help them during their maintenance and evolution tasks. To provide useful information about the purpose of code entities, informal documentation (Stack Overflow) has been shown to be an important source of information that can be leveraged. In this study, we investigate bug reports as a type of informal documentation and we apply machine learning to produce summaries of code entities (methods and classes) in bug reports. In the proposed approach, code entities are extracted using a technique in a form of an island parser that we implemented to identify code in bug reports. Additionally, we applied machine learning to select a set of useful sentences that will be part of the code entities’ summaries. We have used logistic regression as our machine learning technique to rank sentences based on their importance. To this aim, a corpus of sentences is built based on the occurrence of code entities in the sentences belonging to bug reports containing the code entities in question. In the last step, summaries have been evaluated using surveys to estimate the quality of produced summaries. The results show that the automatically produced summaries can reduce time and effort to understand the usage of code entities. Specifically, the majority of participants found summaries extremely helpful to decrease the understanding time (43.5%) and the effort to understand the code entities (39.1%). In the future, summaries can be produced by using other informal documentation such as mailing lists or stack overflow, etc. Additionally, the approach can be applied in practical settings. Consequently, it can be used within an IDE such as Eclipse to assist developers during their software maintenance and evolution tasks

Natural Language Processing for Technology Foresight Summarization and Simplification: the case of patents

Technology foresight aims to anticipate possible developments, understand trends, and identify technologies of high impact. To this end, monitoring emerging technologies is crucial. Patents -- the legal documents that protect novel inventions -- can be a valuable source for technology monitoring. Millions of patent applications are filed yearly, with 3.4 million applications in 2021 only. Patent documents are primarily textual documents and disclose innovative and potentially valuable inventions. However, their processing is currently underresearched. This is due to several reasons, including the high document complexity: patents are very lengthy and are written in an extremely hard-to-read language, which is a mix of technical and legal jargon. This thesis explores how Natural Language Processing -- the discipline that enables machines to process human language automatically -- can aid patent processing. Specifically, we focus on two tasks: patent summarization (i.e., we try to reduce the document length while preserving its core content) and patent simplification (i.e., we try to reduce the document's linguistic complexity while preserving its original core meaning). We found that older patent summarization approaches were not compared on shared benchmarks (making thus it hard to draw conclusions), and even the most recent abstractive dataset presents important issues that might make comparisons meaningless. We try to fill both gaps: we first document the issues related to the BigPatent dataset and then benchmark extractive, abstraction, and hybrid approaches in the patent domain. We also explore transferring summarization methods from the scientific paper domain with limited success. For the automatic text simplification task, we noticed a lack of simplified text and parallel corpora. We fill this gap by defining a method to generate a silver standard for patent simplification automatically. Lay human judges evaluated the simplified sentences in the corpus as grammatical, adequate, and simpler, and we show that it can be used to train a state-of-the-art simplification model. This thesis describes the first steps toward Natural Language Processing-aided patent summarization and simplification. We hope it will encourage more research on the topic, opening doors for a productive dialog between NLP researchers and domain experts.Technology foresight aims to anticipate possible developments, understand trends, and identify technologies of high impact. To this end, monitoring emerging technologies is crucial. Patents -- the legal documents that protect novel inventions -- can be a valuable source for technology monitoring. Millions of patent applications are filed yearly, with 3.4 million applications in 2021 only. Patent documents are primarily textual documents and disclose innovative and potentially valuable inventions. However, their processing is currently underresearched. This is due to several reasons, including the high document complexity: patents are very lengthy and are written in an extremely hard-to-read language, which is a mix of technical and legal jargon. This thesis explores how Natural Language Processing -- the discipline that enables machines to process human language automatically -- can aid patent processing. Specifically, we focus on two tasks: patent summarization (i.e., we try to reduce the document length while preserving its core content) and patent simplification (i.e., we try to reduce the document's linguistic complexity while preserving its original core meaning). We found that older patent summarization approaches were not compared on shared benchmarks (making thus it hard to draw conclusions), and even the most recent abstractive dataset presents important issues that might make comparisons meaningless. We try to fill both gaps: we first document the issues related to the BigPatent dataset and then benchmark extractive, abstraction, and hybrid approaches in the patent domain. We also explore transferring summarization methods from the scientific paper domain with limited success. For the automatic text simplification task, we noticed a lack of simplified text and parallel corpora. We fill this gap by defining a method to generate a silver standard for patent simplification automatically. Lay human judges evaluated the simplified sentences in the corpus as grammatical, adequate, and simpler, and we show that it can be used to train a state-of-the-art simplification model. This thesis describes the first steps toward Natural Language Processing-aided patent summarization and simplification. We hope it will encourage more research on the topic, opening doors for a productive dialog between NLP researchers and domain experts

Multi-Document Summarisation from Heterogeneous Software Development Artefacts

Software engineers create a vast number of artefacts during project development; activities, consisting of related information exchanged between developers. Sifting a large amount of information available within a project repository can be time-consuming. In this dissertation, we proposed a method for multi-document summarisation from heterogeneous software development artefacts to help software developers by automatically generating summaries to help them target their information needs. To achieve this aim, we first had our gold-standard summaries created; we then characterised them, and used them to identify the main types of software artefacts that describe developers’ activities in GitHub project repositories. This initial step was important for the present study, as we had no prior knowledge about the types of artefacts linked to developers’ activities that could be used as sources of input for our proposed multi-document summarisation techniques. In addition, we used the gold-standard summaries later to evaluate the quality of our summarisation techniques. We then developed extractive-based multi- document summarisation approaches to automatically summarise software development artefacts within a given time frame by integrating techniques from natural language processing, software repository mining, and data-driven search-based software engineering. The generated summaries were then evaluated in a user study to investigate whether experts considered that the generated summaries mentioned every important project activity that appeared in the gold-standard summaries. The results of the user study showed that generating summaries from different kinds of software artefacts is possible, and the generated summaries are useful in describing a project’s development activities over a given time frame. Finally, we investigated the potential of using source code comments for summarisation by assessing the documented information of Java primitive variables in comments against three types of knowledge. Results showed that the source code comments did contain additional information and could be useful for summarisation of developers’ development activities.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

Enhancing extractive summarization with automatic post-processing

Tese de doutoramento, Informática (Ciência da Computação), Universidade de Lisboa, Faculdade de Ciências, 2015Any solution or device that may help people to optimize their time in doing productive work is of a great help. The steadily increasing amount of information that must be handled by each person everyday, either in their professional tasks or in their personal life, is becoming harder to be processed. By reducing the texts to be handled, automatic text summarization is a very useful procedure that can help to reduce significantly the amount of time people spend in many of their reading tasks. In the context of handling several texts, dealing with redundancy and focusing on relevant information the major problems to be addressed in automatic multi-document summarization. The most common approach to this task is to build a summary with sentences retrieved from the input texts. This approach is named extractive summarization. The main focus of current research on extractive summarization has been algorithm optimization, striving to enhance the selection of content. However, gains related to the increasing of algorithms complexity have not yet been proved, as the summaries remain difficult to be processed by humans in a satisfactory way. A text built fromdifferent documents by extracting sentences fromthemtends to form a textually fragile sequence of sentences, whose elements tend to be weakly related. In the present work, tasks that modify and relate the summary sentences are combined in a post-processing procedure. These tasks include sentence reduction, paragraph creation and insertion of discourse connectives, seeking to improve the textual quality of the final summary to be delivered to human users. Thus, this dissertation addresses automatic text summarization in a different perspective, by exploring the impact of the postprocessing of extraction-based summaries in order to build fluent and cohesive texts and improved summaries for human usage.Qualquer solução ou dispositivo que possa ajudar as pessoas a optimizar o seu tempo, de forma a realizar tarefas produtivas, é uma grande ajuda. A quantidade de informação que cada pessoa temque manipular, todos os dias, seja no trabalho ou na sua vida pessoal, é difícil de ser processada. Ao comprimir os textos a serem processados, a sumarização automática é uma tarefa muito útil, que pode reduzir significativamente a quantidade de tempo que as pessoas despendem em tarefas de leitura. Lidar com a redundância e focar na informação relevante num conjunto de textos são os principais objectivos da sumarização automática de vários documentos. A abordagem mais comum para esta tarefa consiste em construirse o resumo com frases obtidas a partir dos textos originais. Esta abordagem é conhecida como sumarização extractiva. O principal foco da investigação mais recente sobre sumarização extrativa é a optimização de algoritmos que visam obter o conteúdo relevante expresso nos textos originais. Porém, os ganhos relacionados com o aumento da complexidade destes algoritmos não foram ainda comprovados, já que os sumários continuam a ser difíceis de ler. É expectável que um texto, cujas frases foram extraídas de diferentes fontes, forme uma sequência frágil, sobretudo pela falta de interligação dos seus elementos. No contexto deste trabalho, tarefas que modificam e relacionam frases são combinadas numprocedimento denominado pós-processamento. Estas tarefas incluem a simplificação de frases, a criação de parágrafos e a inserção de conectores de discurso, que juntas procurammelhorar a qualidade do sumário final. Assim, esta dissertação aborda a sumarização automática numa perspectiva diferente, estudando o impacto do pós-processamento de um sumário extractivo, a fim de produzir um texto final fluente e coeso e em vista de se obter uma melhor qualidade textual.Fundação para a Ciência e a Tecnologia (FCT), SFRH/BD/45133/200

Evaluating selected properties of underutilized hardwood species for fabrication of cross-laminated timber industrial mats

Softwood is more in demand than hardwood because it is used primarily in the US\u27s largest wood-consuming industry, construction, resulting in increased importation of softwood annually to meet this demand. Hardwood, used for non-structural purposes like furniture and interior designs, is more abundant in US forests. However, some hardwood species are underutilized and undervalued. Cross-laminated timber (CLT) has increased the demand for softwood. A study evaluated the mechanical and physical properties of three underutilized hardwood species (321 yellow poplar, 393 sweetgum, and 262 red oak specimens) for CLT industrial mat manufacturing. The results showed that red oak had a higher density than southern yellow pine, and all species had an average modulus of elasticity greater than the CLT lumber requirement. The study confirmed the viability of these underutilized hardwoods for CLT fabrication, suggesting they could be a suitable substitute for softwood in CLT manufacturing