Improving Multi-Document Summary Method Based on Sentence Distribution

Automatic multi-document summaries had been developed by researchers. The method used to select sentences from the source document would determine the quality of the summary result. One of the most popular methods used in weighting sentences was by calculating the frequency of occurrence of words forming the sentences. However, choosing sentences with that method could lead to a chosen sentence which didn't represent the content of the source document optimally. This was because the weighting of sentences was only measured by using the number of occurrences of words. This study proposed a new strategy of weighting sentences based on sentences distribution to choose the most important sentences which paid much attention to the elements of sentences that were formed as a distribution of words. This method of sentence distribution enables the extraction of an important sentence in multi-document summarization which served as a strategy to improve the quality of sentence summaries. In that respect were three concepts used in this study: (1) clustering sentences with similarity based histogram clustering, (2) ordering cluster by cluster importance and (3) selection of important sentence by sentence distribution. Results of experiments showed that the proposed method had a better performance when compared with SIDeKiCK and LIGI methods. Results of ROUGE-1 showed the proposed method increasing 3% compared with the SIDeKiCK method and increasing 5.1% compared with LIGI method. Results of ROUGE-2 proposed method increase 13.7% compared with the SIDeKiCK and increase 14.4% compared with LIGI method

Peringkasan Dokumen Berbahasa Inggris Menggunakan Sebaran Local Sentence

. The number of digital documents grows very rapidly causing time waste in searching and reading the information. To overcome these problems, many document summary methods are developed to find important or key sentences from the source document. This study proposes a new strategy in summarizing English document by using local sentence distribution method to find and dig up hidden important sentence from the source document in an effort to improve quality of the summaries. Experiments are conducted on dataset DUC 2004 task 2. Measurement ROUGE-1 and ROUGE-2 are employed as a performance evaluation of the proposed method with sentence information density and sentence cluster keyword (SIDeKiCK). The experiment shows that the proposed method has better performance with an average achievement ROUGE-1 0.398, an increase of 1.5% compared to SIDeKiCK method and ROUGE-2 0.12, an increase 13% compared to SIDeKiCK method

Opinion Mining Summarization and Automation Process: A Survey

In this modern age, the internet is a powerful source of information. Roughly, one-third of the world population spends a significant amount of their time and money on surfing the internet. In every field of life, people are gaining vast information from it such as learning, amusement, communication, shopping, etc. For this purpose, users tend to exploit websites and provide their remarks or views on any product, service, event, etc. based on their experience that might be useful for other users. In this manner, a huge amount of feedback in the form of textual data is composed of those webs, and this data can be explored, evaluated and controlled for the decision-making process. Opinion Mining (OM) is a type of Natural Language Processing (NLP) and extraction of the theme or idea from the user's opinions in the form of positive, negative and neutral comments. Therefore, researchers try to present information in the form of a summary that would be useful for different users. Hence, the research community has generated automatic summaries from the 1950s until now, and these automation processes are divided into two categories, which is abstractive and extractive methods. This paper presents an overview of the useful methods in OM and explains the idea about OM regarding summarization and its automation process

A framework for the Comparative analysis of text summarization techniques

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceWe see that with the boom of information technology and IOT (Internet of things), the size of information which is basically data is increasing at an alarming rate. This information can always be harnessed and if channeled into the right direction, we can always find meaningful information. But the problem is this data is not always numerical and there would be problems where the data would be completely textual, and some meaning has to be derived from it. If one would have to go through these texts manually, it would take hours or even days to get a concise and meaningful information out of the text. This is where a need for an automatic summarizer arises easing manual intervention, reducing time and cost but at the same time retaining the key information held by these texts. In the recent years, new methods and approaches have been developed which would help us to do so. These approaches are implemented in lot of domains, for example, Search engines provide snippets as document previews, while news websites produce shortened descriptions of news subjects, usually as headlines, to make surfing easier. Broadly speaking, there are mainly two ways of text summarization – extractive and abstractive summarization. Extractive summarization is the approach in which important sections of the whole text are filtered out to form the condensed form of the text. While the abstractive summarization is the approach in which the text as a whole is interpreted and examined and after discerning the meaning of the text, sentences are generated by the model itself describing the important points in a concise way

Grouping sentences as better language unit for extractive text summarization

Most existing methods for extractive text summarization aim to extract important sentences with statistical or linguistic techniques and concatenate these sentences as a summary. However, the extracted sentences are usually incoherent. The problem becomes worse when the source text and the summary are long and based on logical reasoning. The motivation of this paper is to answer the following two related questions: What is the best language unit for constructing a summary that is coherent and understandable? How is the extractive summarization process based on the language unit? Extracting larger language units such as a group of sentences or a paragraph is a natural way to improve the readability of summary as it is rational to assume that the original sentences within a larger language unit are coherent. This paper proposes a framework for group-based text summarization that clusters semantically related sentences into groups based on Semantic Link Network (SLN) and then ranks the groups and concatenates the top-ranked ones into a summary. A two-layer SLN model is used to generate and rank groups with semantic links including the is-part-of link, sequential link, similar-to link, and cause–effect link. The experimental results show that summaries composed by group or paragraph tend to contain more key words or phrases than summaries composed by sentences and summaries composed by groups contain more key words or phrases than those composed by paragraphs especially when the average length of source texts is from 7000 words to 17,000 words which is the usual length of scientific papers. Further, we compare seven clustering algorithms for generating groups and propose five strategies for generating groups with the four types of semantic links

Provision of better VLE learner support with a Question Answering System

The focus of this research is based on the provision of user support to students using electronic means of communication to aid their learning. Digital age brought anytime anywhere access of learning resources to students. Most academic institutions and also companies use Virtual Learning Environments to provide their learners with learning material. All learners using the VLE have access to the same material and help despite their existing knowledge and interests. This work uses the information in the learning materials of Virtual Learning Environments to answer questions and provide student help by a Question Answering System. The aim of this investigation is to research if a satisfactory combination of Question Answering, Information Retrieval and Automatic Summarisation techniques within a VLE will help/support the student better than existing systems (full text search engines)