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Background:
Summarization is a process to select important information from a source text. Summarizing strategies are the core cognitive processes in summarization activity. Since summarization can be important as a tool to improve comprehension, it has attracted interest of teachers for teaching summary writing through direct instruction. To do t...
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This study explores the characteristics of the lexical terms risk and hazard in European Union (EU) documents. The documents contain text written for the specific purpose of Risk Assessment. The aim of this corpus-based research is to describe risk and hazard in the context of the document, and to determine whether the terms are used consistently....
Citations
... For the last stage, according to the literature review, there are four types of feedback, viz. providing scores [24,28,46,98], peer review [104], section content coverage [24,28,98] and summary writing strategy detection [1,28,51]. However, for the same reasons as concept maps, the coverage of chapter content that requires instructor annotation does not apply to academic writing scenarios. ...
The significance of novice researchers acquiring proficiency in writing abstracts has been extensively documented in the field of higher education, where they often encounter challenges in this process. Traditionally, students have been advised to enroll in writing training courses as a means to develop their abstract writing skills. Nevertheless, this approach frequently falls short in providing students with personalized and adaptable feedback on their abstract writing. To address this gap, we initially conducted a formative study to ascertain the user requirements for an abstract writing training tool. Subsequently, we proposed a domain-specific abstract writing training tool called ALens, which employs rhetorical structure parsing to identify key concepts, evaluates abstract drafts based on linguistic features, and employs visualization techniques to analyze the writing patterns of exemplary abstracts. A comparative user study involving an alternative abstract writing training tool has been conducted to demonstrate the efficacy of our approach.
... Research on summary writing with English as a foreign language (EFL) and English as a second language (ESL) students in the Association of Southeast Asian Nations (ASEAN) countries tends to focus on summary writing instruction and identification of summary writing strategies (Abdi, Idris, Alguliyev, & Aliguliyev, 2016;Idris, Baba, & Abdullah, 2011;McDonough, Crawford, & De Vleeschauwer, 2014;Wichadee, 2014). In most ASEAN countries, where the English language is taught either as EFL or ESL, many students appear to have poor summary writing skills (Cho, 2012;Choy & Lee, 2012). ...
... Wichadee's (2014) study, which compared EFL students who received instruction in transactional strategies with those taught by the traditional method, showed that transactional strategies enhanced their summarizing skills. Abdi et al. (2016) and Idris et al. (2011) developed algorithm-based summarization assessment systems to identify the ESL students' summarizing strategies to improve comprehension and summary writing. ...
... Hence, there is a need to activate the students' prior knowledge to enable them to relate the source text to their existing knowledge while writing the summary. However, prior knowledge activation is absent or underrepresented in most of the studies related to summary writing (Abdi et al., 2016;Choy & Lee, 2012;Friend, 2001;Gao, 2013;Idris et al., 2011;Wichadee, 2014). Friend (2001), Choy and Lee (2012), and Wichadee (2014) investigated the effects of strategy instruction on summary writing, whereas Gao (2013) examined the effects of summary writing on students' reading comprehension. ...
The purpose of this study was to design and develop a theory-based summary
writing online tool, named Summary Writing-Pal (SW-PAL), to assist English as a
second language students in improving their summary writing. It also evaluates the
effectiveness of SW-PAL in enhancing the students’ summary writing performance
and examines their perceptions about it. This mixed-method empirical study involved
53 English as a second language students majoring in computer science at a local
university. Two intact groups were randomly chosen as the control and experimental
groups with 26 and 27 students, respectively. The control group was taught using the
conventional method, while the experimental group was taught using SW-PAL. Both
groups were given a pre- and post-summary writing test. A Split-Plot Analysis of
Covariance test was used to examine the effectiveness of the SW-PAL tool. A focus
group interview was conducted to gather qualitative data on perceptions about the
SW-PAL tool. Quantitative findings revealed that students’ summary writing performance
improved significantly due to the SW-PAL with a large effect size of .42. Qualitativewise, the users perceived SW-PAL to be useful as a motivating, challenging,
and self-learning tool. Recommendations for practice for language instructors who
wish to incorporate such a tool into their language instruction and suggestions for
future research are discussed.
... Secondary and tertiary institutions throughout ASEAN countries have adopted summary writing as a measurement to assess the ability of students in comprehending texts in the English language. Although a substantial number of studies have investigated summary writing, only a handful have initiated innovative techniques that have led to summary writing enhancement (Abdi, Idris, Alguliyev, & Aliguliyev, 2016;Cho, 2012;Friend, 2001;Idris, Baba, & Abdullah, 2011;Ke & Hoey, 2014;Marzec-Stawiarska, 2016;McDonough, Crawford, & De Vleeschauwer, 2014;Sung, Liao, Chang, Chen, & Chang, 2016;Wichadee, 2014;Yang, 2015). Obviously, some students have poor summary writing skill, particularly among English as a second language (ESL) students (Hosseinpur, 2015;Idris et al., 2011;Kim, 2001;McDonough et al., 2014;Wichadee, 2014). ...
... Apart from prior knowledge, one key area related to summary writing skills refers to identifying commonly used summarizing strategies. Several researchers have identified a number of effective summarizing strategies applied by both students and teachers (Abdi et al., 2016;Idris et al., 2011), such as topic sentence selection, deletion, sentence combination, paraphrasing, generalization, and invention. The outcomes from these studies signify that mastery of summarizing strategies aids in producing good summaries. ...
... Recent interest of researches concerning summary writing has embedded computer technology in the education thread (Abdi et al., 2016;Idris et al., 2011;Sung et al., 2016;Wade-Stein & Kintsch, 2004). Vast research outcomes have typically proven that learners achieve learner autonomy in language learning and significant learning gains via computer-assisted learning (CAL) environment (Mukama, 2009;Ridgway, 1986;Wang, Shang, & Briody, 2013). ...
Lay Description
What is already known about this topic: Currently, there has been a lot of interest in CAL English language and numerous summary writing tools have been developed for language learning and teaching.
Existing summary writing tools did not focus on learning theories incorporation.
Worked examples approach is effective in well‐defined domain (mathematics, physics, etc.), how about if apply in ill‐defined domain (summary writing)?
What this paper adds: Design and develop a CAL environment for summary writing.
Incorporate learning theories in CAL environment.
Apply worked example instructional approach in learning summary writing.
Implications for practice and/or policy: Conventional teaching versus CAL environment in summary writing: CAL environment achieved better performance.
Worked examples in ill‐defined domain (summary writing) are also effective in language learning.
Worked examples is more effective for lower English language proficiency students.
Cognitive load: Lower language proficiency students demonstrated lower cognitive load when using CAL environment.
... It is an important way of finding relevant information in large text libraries or in the Internet (Canhasi & Kononenko, 2014;Ferreira et al., 2014). Text summarization can help users to access the information more easily, from one hand, reducing the time they have to spend dealing with the information and, on the other hand, selecting the information most useful for them (Abdi, Idris, Alguliev, & Aliguliyev, 2015;Abdi, Idris, Alguliyev, & Aliguliyev, 2016;Lloret & Palomar, 2013;Yang & Wang, 2008). ...
Text summarization is a process of extracting salient information from a source text and presenting that information to the user in a condensed form while preserving its main content. In the text summarization, most of the difficult problems are providing wide topic coverage and diversity in a summary. Research based on clustering, optimization, and evolutionary algorithm for text summarization has recently shown good results, making this a promising area. In this paper, for a text summarization, a two‐stage sentences selection model based on clustering and optimization techniques, called COSUM, is proposed. At the first stage, to discover all topics in a text, the sentences set is clustered by using k‐means method. At the second stage, for selection of salient sentences from clusters, an optimization model is proposed. This model optimizes an objective function that expressed as a harmonic mean of the objective functions enforcing the coverage and diversity of the selected sentences in the summary. To provide readability of a summary, this model also controls length of sentences selected in the candidate summary. For solving the optimization problem, an adaptive differential evolution algorithm with novel mutation strategy is developed. The method COSUM was compared with the 14 state‐of‐the‐art methods: DPSO‐EDASum; LexRank; CollabSum; UnifiedRank; 0–1 non‐linear; query, cluster, summarize; support vector machine; fuzzy evolutionary optimization model; conditional random fields; MA‐SingleDocSum; NetSum; manifold ranking; ESDS‐GHS‐GLO; and differential evolution, using ROUGE tool kit on the DUC2001 and DUC2002 data sets. Experimental results demonstrated that COSUM outperforms the state‐of‐the‐art methods in terms of ROUGE‐1 and ROUGE‐2 measures.
... The most frequent terms in a source text include relevant information and can be indicative of the document's topic (A. Abdi, Idris, Alguliyev, & Aliguliyev, 2016;Nenkova, Vanderwende, & McKeown, 2006;Neto, Freitas, & Kaestner, 2002). An important sentence can be determined by counting the number of significant words in a sentence (Neto, et al., 2002;C. ...
... According to previous studies, the authors usually present the main idea in certain sections such as, at the beginning of the paragraphs or the opening paragraphs, etc.(A. Abdi, et al., 2016;Neto, et al., 2002;C. S. Yadav & Sharan, 2015). ...
... In our work, in order to consider "Cue method", we collected a set of cue words from previous studies, Table 3 (A. Abdi, et al., 2016;Alonso, 2005;Fraser, 1999;Knott, 1996). In the current function, if a sentence contains a cue word, the C (S)= 1; otherwise the C (S) =0. ...
Sentiment summarization is the process of automatically creating a compressed version of the opinionated information expressed in a text. This paper presents a machine learning-based approach to summarize user’s opinion expressed in reviews using: 1)Sentiment knowledge to calculate a sentence sentiment score as one of the features for sentence-level classification. It integratesmultiple strategies to tackle the following problems: sentiment shifter, the types of sentencesand word coverage limit. 2)Word embedding model, a deep-learning-inspired method to understand meaning and semantic relationships among wordsand to extract avector representation for each word. 3)Statistical and linguistic knowledge todetermine salientsentences.The proposed method combines several types of features into a unified feature set to design a more accurate classification system(“True”: the extractive reference summary; “False”: otherwise). Thus, to achieve better performance scores,we carried out a performance study of four well-known feature selection techniques and seven of themostfamous classifiers to select themost relevant set of features and find an efficient machine learning classifier, respectively.The proposed method is applied to three different datasets and the results show the integration of support vector machine-basedclassificationmethod and Information Gain (IG) as a feature selection technique can significantly improve the performance and make the method comparable to other existing methods.Furthermore, our method that learns from this unified feature set can obtain better performance thanone that learns from a feature subset
... One of the key areas related to summarization skills is identifying summarizing strategies. Few researchers have focused on identifying summarizing strategies by students and teachers [2]- [4]. These studies were able to identify summarizing strategies such as Topic Sentences Selection, Deletion, Sentences Combination, Copy-paste, Off-the-subject and Paraphrase. ...
... Apart from that, researchers have shown that summarizing strategies play an important role in producing a good summary [2], [3], [13], [14]. Students have to be given proper guidelines and instructions while learning summarizing strategies. ...
... Furthermore, concept mapping is proposed to be used as an advance organizer tool to activate students' prior knowledge in text reading. The last component, Summarizing component is integrated with Abdi et al. [2], the Summarizing Strategies Identification based on Linguistic Knowledge (ISSLK) algorithm that used to identify the used summarizing strategies. ...
... In consequence, more writing can be assigned, resulting in more instruction and practice for students (Shermis et al. 2016). Another major purpose of using automated scoring is to understand reading comprehension and writing processes through the lens of linguistic variables and text difficulty (Abdi et al. 2016;McNamara et al. 2014). ...
The problem of poor writing skills at the postsecondary level is a large and troubling one. This study investigated the writing skills of low-skilled adults attending college developmental education courses by determining whether variables from an automated scoring system were predictive of human scores on writing quality rubrics. The human-scored measures were a holistic quality rating for a persuasive essay and an analytic quality score for a written summary. Both writing samples were based on text on psychology and sociology topics related to content taught at the introductory undergraduate level. The study is a modified replication of McNamara et al. (Written Communication, 27(1), 57–86 2010), who identified several Coh-Metrix variables from five linguistic classes that reliably predicted group membership (high versus low proficiency) using human quality scores on persuasive essays written by average-achieving college students. When discriminant analyses and ANOVAs failed to replicate the McNamara et al. (Written Communication, 27(1), 57–86 2010) findings, the current study proceeded to analyze all of the variables in the five Coh-Metrix classes. This larger analysis identified 10 variables that predicted human-scored writing proficiency. Essay and summary scores were predicted by different automated variables. Implications for instruction and future use of automated scoring to understand the writing of low-skilled adults are discussed.
Abstract
Background: The online paraphrasing tool aids in the online rewriting of articles and
essays. Users may avoid plagiarism by rephrasing sentences.
Scope: In this study focus on the awareness and use of paraphrasing tools by the
Research scholars of Babasaheb Bhimrao Ambedkar University, situated in Lucknow,
Uttar Pradesh, and the Central University of South Bihar, situated in Gaya, Bihar.
Method: Survey methodology and online questionnaire tools used in this paper.
Data collection: 200 responses were received from the researchers and scholars at
BBAU and CUSB.
Finding: The finding of this study showed that research scholar of BBAU and CUSB are
aware about the paraphrasing tools and they use more than one paraphrasing tool to
manage their work. Quillbot and Duplicheaker paraphrasing tools are most used tools by
research scholars of BBAU and CUSB.
Suggestion: The study suggest that need a training program on Paraphrasing tool for
proper utilization of tools in best ethical manner.
This paper presents an automatic sentiment-oriented summarization of multi-documents using soft computing (called ASMUS). It integrates two main phases: sentiment analysis and sentiment summarization. Sentiment analysis phase includes multiple strategies to tackle the following drawbacks: (1) word coverage limit of an individual lexicon; (2) contextual polarity; (3) sentence types, while the sentiment summarization phase is a graph-based ranking model that integrates the sentiment information, statistical and linguistic methods to improve the sentence ranking result. We found that the current methods are suffering from the following problems: (1) they do not consider the semantic and syntactic information in comparison between two sentences when they share the similar bag-of-words (capturing meaning); (2) vocabulary mismatch problem (lexical gaps). Furthermore, ASMUS also considers content coverage and redundancy. We conduct the experiments on the Document Understanding Conference datasets. The results present the excellent outcomes of the ASMUS in sentiment-oriented summarization.
For the achievement of GOLD with entry titled: ”SUMMARULE: Relevance Detection & Summarizing Strategies Identification Tool”
