Sentence Ranking Research Articles

Deep Neural Network Based Multi-Review Summarization System

E-commerce is prevalent everywhere now-a-days. While shopping from these sites, users generally go through the reviews of the product posted by other users. For a given product, thousands of reviews may be available and it is cumbersome for the user to analyze each and every review. This paper proposes a multi-review summarization method to get a summarized review of products. A deep neural network-based model is employed to create an extractive summary of the reviews collected from online e-commerce sites i.e. Amazon and Flipkart. The deep neural network has been used to obtain the features of the product from multi reviews and cluster the sentences based on learned features. After clustering, a ranking of sentences is done and hence, an extractive summary is generated by selecting top n sentences from each of the clusters formed.

TSQA: Tabular Scenario Based Question Answering

Scenario-based question answering (SQA) has attracted an increasing research interest. Compared with the well-studied machine reading comprehension (MRC), SQA is a more challenging task: a scenario may contain not only a textual passage to read but also structured data like tables, i.e., tabular scenario based question answering (TSQA). AI applications of TSQA such as answering multiple-choice questions in high-school exams require synthesizing data in multiple cells and combining tables with texts and domain knowledge to infer answers. To support the study of this task, we construct GeoTSQA. This dataset contains 1k real questions contextualized by tabular scenarios in the geography domain. To solve the task, we extend state-of-the-art MRC methods with TTGen, a novel table-to-text generator. It generates sentences from variously synthesized tabular data and feeds the downstream MRC method with the most useful sentences. Its sentence ranking model fuses the information in the scenario, question, and domain knowledge. Our approach outperforms a variety of strong baseline methods on GeoTSQA.

An Integrated Approach for Improving Brand Consistency of Web Content

A consumer-dependent (business-to-consumer) organization tends to present itself as possessing a set of human qualities, which is termed the brand personality of the company. The perception is impressed upon the consumer through the content, be it in the form of advertisement, blogs, or magazines, produced by the organization. A consistent brand will generate trust and retain customers over time as they develop an affinity toward regularity and common patterns. However, maintaining a consistent messaging tone for a brand has become more challenging with the virtual explosion in the amount of content that needs to be authored and pushed to the Internet to maintain an edge in the era of digital marketing. To understand the depth of the problem, we collect around 300K web page content from around 650 companies. We develop trait-specific classification models by considering the linguistic features of the content. The classifier automatically identifies the web articles that are not consistent with the mission and vision of a company and further helps us to discover the conditions under which the consistency cannot be maintained. To address the brand inconsistency issue, we then develop a sentence ranking system that outputs the top three sentences that need to be changed for making a web article more consistent with the company’s brand personality.

A Heuristic Approach for Telugu Text Summarization with Improved Sentence Ranking

Extracting/abstracting the condensed form of original text document by retaining its information and complete meaning is known as text summarization. The creation of manual summaries from large text documents is difficult and time-consuming for humans. Text summarization has become an important and challenging area in natural language processing. This paper presents a heuristic appraoch to extract a summary of e-news articles of the Telugu language. The method proposes new lexical parameter-based information extraction (IE) rules for scoring the sentences. Event score and Named Entity Score is a novel part in sentence scoring to identify the essential information in the text. Depending on the frequency of occurrence of event/named entites in the sentence and document, sentences are selected for summary. Data is collected from online news sources (i.e., Eenadu, Sakshi,Andhra Jyothi, Namaste Telangana) to experiment. The proposed method is compared with other techniques developed for Telugu text summarization. Evaluation metrics like precision, recall, and F1 score is used to measure the proposed method's performance. An extensive statistical and qualitative evaluation of the system's summaries has been conducted using Recall-Oriented Understudy for Gisting Evaluation (ROUGE), a standard summary evaluation tool. The results showed improved performance compared to other methods.

A Novel Framework for Multi-Document Temporal Summarization (MDTS)

Internet or Web consists of a massive amount of information, handling which is a tedious task. Summarization plays a crucial role in extracting or abstracting key content from multiple sources with its meaning contained, thereby reducing the complexity in handling the information. Multi-document summarization gives the gist of the content collected from multiple documents. Temporal summarization concentrates on temporally related events. This paper proposes a Multi-Document Temporal Summarization (MDTS) technique that generates the summary based on temporally related events extracted from multiple documents. This technique extracts the events with the time stamp. TIMEML standards tags are used in extracting events and times. These event-times are stored in a structured database form for easier operations. Sentence ranking methods are build based on the frequency of events occurrences in the sentence. Sentence similarity measures are computed to eliminate the redundant sentences in an extracted summary. Depending on the required summary length, top-ranked sentences are selected to form the summary. Experiments are conducted on DUC 2006 and DUC 2007 data set that was released for multi-document summarization task. The extracted summaries are evaluated using ROUGE to determine precision, recall and F measure of generated summaries. The performance of the proposed method is compared with particle swarm optimization-based algorithm (PSOS), Cat swarm optimization-based summarization (CSOS), Cuckoo Search based multi-document summarization (MDSCSA). It is found that the performance of MDTS is better when compared with other methods. Doi: 10.28991/esj-2021-01268 Full Text: PDF

Automatic Text Summarization of COVID-19 Research Articles Using Recurrent Neural Networks and Coreference Resolution

Purpose: Pandemic COVID-19 has created an emergency for the medical community. Researchers require extensive study of scientific literature in order to discover drugs and vaccines. In this situation where every minute is valuable to save the lives of hundreds of people, a quick understanding of scientific articles will help the medical community. Automatic text summarization makes this possible. Materials and Methods: In this study, a recurrent neural network-based extractive summarization is proposed. The extractive method identifies the informative parts of the text. Recurrent neural network is very powerful for analyzing sequences such as text. The proposed method has three phases: sentence encoding, sentence ranking, and summary generation. To improve the performance of the summarization system, a coreference resolution procedure is used. Coreference resolution identifies the mentions in the text that refer to the same entity in the real world. This procedure helps to summarization process by discovering the central subject of the text. Results: The proposed method is evaluated on the COVID-19 research articles extracted from the CORD-19 dataset. The results show that the combination of using recurrent neural network and coreference resolution embedding vectors improves the performance of the summarization system. The Proposed method by achieving the value of ROUGE1-recall 0.53 demonstrates the improvement of summarization performance by using coreference resolution embedding vectors in the RNN-based summarization system. Conclusion: In this study, coreference information is stored in the form of coreference embedding vectors. Jointly use of recurrent neural network and coreference resolution results in an efficient summarization system.

Comparison of Data Augmentation Methods in Pointer-Generator Model Using Various Sentence Ranking Methods

Comparison of Data Augmentation Methods in Pointer-Generator Model Using Various Sentence Ranking Methods

Text Summarization using Extractive and Abstractive Methods

Text Summarization is a process where a huge text file is converted into summarized version which will preserve the original meaning and context. The main aim of any text summarization is to provide a accurate and precise summary. One approach is to use a sentence ranking algorithm. This comes under extractive summarization. Here, a graph based ranking algorithm is used to rank the sentences in the text and then top k-scored sentences are included in the summary. The most widely used algorithm to decide the importance of any vertex in a graph based on the information retrieved from the graph is Graph Based Ranking Algorithm. TextRank is one of the most efficient ranking algorithms which is used for Web link analysis that is for measuring the importance of website pages. Another approach is abstractive summarization where a LSTM encoder decoder model is used along with attention mechanism which focuses on some important words from the input. Encoder encodes the input sequence and decoder along with attention mechanism gives the summary as the output.

Project Reporting Management System with AI based Assistive Features for Text Summarization

This paper details a proof-of-concept system called Project Reporting Management System (PRMS) to manage the project reporting process in a typical research centre where the process can be manual for many centres. In fact, it is general enough to be scaled up and deployed for a large department or scaled down for a smaller setup in any organization which needs a simple and efficient project progress reporting system but does not entail the kind of complexity and cost of commercial project management systems. Using a research centre scenario, the progress of the individual projects has to be tracked through the periodic submission of progress reports by the Principal Investigator (PI) of the project. The centre will need to consolidate these individual reports manually into a consolidated report and an executive summary for higher management. PRMS automates the tracking of individual projects and reporting deadlines, sends reminders and allows online submission of reports by the PIs. PRMS also incorporates assistive and automated features exploiting Machine Learning (ML) and Natural Language Processing (NLP) techniques to generate the consolidated report and rank sentences of verbose report for assistive text summarization to facilitate the manual process of producing an executive summary.

문장 랭킹 스코어와 그래프 기법을 사용한 질의 기반 생성 요약 모델

기본적인 생성 요약 모델은 문서 내 모든 중요 내용을 포함하는 짧은 요약문을 생성하는 것을 목표로 한다. 반면, 질의(Query) 기반 생성 요약 모델의 경우 문서 내에서 질의와 관련된 정보를 요약해야 한다. 기존의 질의 기반 요약 모델은 문서 내 단어들과 질의문 간의 어텐션(Attention) 메커니즘을 통해 단어의 가중치를 계산하고 이를 기반으로 문장의 중요도를 계산한다. 이러한 방식은 문서의 전체적인 문맥정보를 반영하기 어렵다는 단점이 있다. 본 논문에서는 이러한 문제를 문장 랭킹 스코어와 문장 단위 그래프 구조를 만들어 문장의 중요도뿐만 아니라 문맥 정보를 반영하여 생성 요약의 성능을 향상시킬 수 있는 새로운 생성 요약 기법을 제안한다. 실험으로 살펴본 최종 제안 모델의 성능은 같은 데이터를 사용하는 선행 모델 대비 ROUGE-1 1.44%p, ROUGE-L 0.52%p의 향상된 성능을 보인다.

CERC: an interactive content extraction, recognition, and construction tool for clinical and biomedical text

BackgroundAutomated summarization of scientific literature and patient records is essential for enhancing clinical decision-making and facilitating precision medicine. Most existing summarization methods are based on single indicators of relevance, offer limited capabilities for information visualization, and do not account for user specific interests. In this work, we develop an interactive content extraction, recognition, and construction system (CERC) that combines machine learning and visualization techniques with domain knowledge for highlighting and extracting salient information from clinical and biomedical text.MethodsA novel sentence-ranking framework multi indicator text summarization, MINTS, is developed for extractive summarization. MINTS uses random forests and multiple indicators of importance for relevance evaluation and ranking of sentences. Indicative summarization is performed using weighted term frequency-inverse document frequency scores of over-represented domain-specific terms. A controlled vocabulary dictionary generated using MeSH, SNOMED-CT, and PubTator is used for determining relevant terms. 35 full-text CRAFT articles were used as the training set. The performance of the MINTS algorithm is evaluated on a test set consisting of the remaining 32 full-text CRAFT articles and 30 clinical case reports using the ROUGE toolkit.ResultsThe random forests model classified sentences as “good” or “bad” with 87.5% accuracy on the test set. Summarization results from the MINTS algorithm achieved higher ROUGE-1, ROUGE-2, and ROUGE-SU4 scores when compared to methods based on single indicators such as term frequency distribution, position, eigenvector centrality (LexRank), and random selection, p < 0.01. The automatic language translator and the customizable information extraction and pre-processing pipeline for EHR demonstrate that CERC can readily be incorporated within clinical decision support systems to improve quality of care and assist in data-driven and evidence-based informed decision making for direct patient care.ConclusionsWe have developed a web-based summarization and visualization tool, CERC (https://newton.isye.gatech.edu/CERC1/), for extracting salient information from clinical and biomedical text. The system ranks sentences by relevance and includes features that can facilitate early detection of medical risks in a clinical setting. The interactive interface allows users to filter content and edit/save summaries. The evaluation results on two test corpuses show that the newly developed MINTS algorithm outperforms methods based on single characteristics of importance.

A new graph-based extractive text summarization using keywords or topic modeling

In graph-based extractive text summarization techniques, the weight assigned to the edges of the graph is the crucial parameter for the sentence ranking. The weights associated with the edges are based on the similarity between sentences (nodes). Most of the graph-based techniques use the common words based similarity measure to assign the weight. In this paper, we propose a new graph-based summarization technique, which, besides taking into account the similarity among the individual sentences, also considers the similarity between the sentences and the overall (input) document. While assigning the weight among the edges of the graph, we consider two attributes. The first attribute is the similarity among the nodes, which forms the edges of the graph. The second attribute is the weight given to a component that represents how much the particular edge is similar to the topics of the overall document for which we incorporate the topic modeling. Along with these modifications, we use the semantic measure to find the similarity among the nodes. The evaluation results of the proposed method demonstrate a significant improvement of the summary quality over the existing text summarization techniques.

LSA Based Text Summarization

In this study we propose an automatic single document text summarization technique using Latent Semantic Analysis (LSA) and diversity constraint in combination. The proposed technique uses the query based sentence ranking. Here we are not considering the concept of IR (Information Retrieval) so we generate the query by using the TF-IDF(Term Frequency-Inverse Document Frequency). For producing the query vector, we identify the terms having the high IDF. We know that LSA utilizes the vectorial semantics to analyze the relationships between documents in a corpus or between sentences within a document and key terms they carry by producing a list of ideas interconnected to the documents and terms. LSA helps to represent the latent structure of documents. For selecting the sentences from the document Latent Semantic Indexing (LSI) is used. LSI helps to arrange the sentences with its score. Traditionally the highest score sentences have been chosen for summary but here we calculate the diversity between chosen sentences and produce the final summary as a good summary should have maximum level of diversity. The proposed technique is evaluated on OpinosisDataset1.0.

A hierarchical self-attentive neural extractive summarizer via reinforcement learning (HSASRL)

In recent years, deep neural extractive-based summarization approaches have achieved vast popularity over conventional approaches. However, previously proposed neural extractive-based models have issues that limit their performance. One of these issues is related to the architecture of the used neural network that skips some details about the document hierarchical structure. Moreover, these models are optimized to maximize the probabilities of the training data ground truth labels rather than the evaluation metric that actually measures the quality of the summarization; this way of optimization might neglect important information related to sentence ranking. To address these issues, we combined reinforcement and supervised learning to train a hierarchical self-attentive reinforced neural network-based summarization model to rank sentences according to their significance by directly optimizing the ROUGE evaluation metric. The proposed model employs a hierarchical self-attention mechanism to generate document and sentence embeddings that reflect the hierarchical structure of the document and give better feature representation. While reinforcement learning enables direct optimization with respect to evaluation metrics, the attention mechanism adds an extra source of information to direct the summary extraction. The model was evaluated on the basis of three well-known datasets, namely, CNN, Daily Mail, and their combined version CNN/Daily Mail. Experimental results showed that the model achieved higher ROUGE scores than state-of-the-art models for extractive summarization on the three datasets.

A novel extractive text summarization system with self-organizing map clustering and entity recognition

Extractive text summarization yields the sensitive parts of the document by neglecting the irrelevant and redundant information. In this paper, we propose a new strategy for extractive single-document summarization in Malayalam. Initially, entity recognition is done, followed by relevance analysis is made based on some context-aware features. The scored sentences are then clustered using self-organizing maps (SOM) and from these clusters, relevant sentences are extracted out based on the proposed algorithm. Both theoretical and practical evaluations are done to analyze the implemented system. In theoretical evaluation, gradient calculations of relevance equations are used to know that which of these sentence scoring features are contributing more. The relevance equation is optimized with the help of Lagrange’s multiplier. The complexity analysis of the proposed algorithms is also performed. In practical evaluation, the system compared with online and offline summarizers upon metrics like precision, recall, and F-measure. The system is tested through a non-clustering approach also in order to analyze the impact of clustering used in our work. Some existing strategies like question game evaluation, sentence rank evaluation, and keyword association are also done to evaluate the different parameters like the relevance of sentences, important entity words, etc.

Sparse two level topic model for extraction of general summary words

Extractive multi-document summarization methods based on topic models find relevant general concepts or topics that are most representative of the documents. These topics are used for sentence ranking and selection. In this paper, a two level topic model using spike and slab prior is proposed that identify better general topics for summarization. Spike and slab prior is used earlier for finding aspect specific topics. Proposed two level model uses spike and slab prior to achieve better general topics at high level of topic hierarchy. Experiments conducted on DUC2007 dataset show that proposed model is able to identify more summary oriented general words and improve ROUGE score.

An unsupervised semantic sentence ranking scheme for text documents

This paper presents Semantic SentenceRank (SSR), an unsupervised scheme for automatically ranking sentences in a single document according to their relative importance. In particular, SSR extracts essential words and phrases from a text document, and uses semantic measures to construct, respectively, a semantic phrase graph over phrases and words, and a semantic sentence graph over sentences. It applies two variants of article-structure-biased PageRank to score phrases and words on the first graph and sentences on the second graph. It then combines these scores to generate the final score for each sentence. Finally, SSR solves a multi-objective optimization problem for ranking sentences based on their final scores and topic diversity through semantic subtopic clustering. An implementation of SSR that runs in quadratic time is presented, and it outperforms, on the SummBank benchmarks, each individual judge’s ranking and compares favorably with the combined ranking of all judges.

Sentiment Analysis and Opinion Summarization of Product Feedback

With the exponential growth of online shopping platforms, user interaction is made direct through their reviews and ratings. User’s opinions and experiences are a significant source of valuable information in decision making process. In recent days, almost every website encourages users to express and exchange their views, suggestions and opinions related to product, services, policies, etc. publicly. Opinion mining is an extensive branch of Artificial Intelligence and a form of Natural Language Processing which illustrates the attitude of the customers, in specific services or products. Also known as Sentiment Analysis, it aims at determining the response and mood or attitude of the speaker or the overall contextual and emotional polarity or reaction. Existing algorithms determine sentiment by training on datasets, lexicon-based approach by calculating polarity and rule-based approach for classification. Opinion Summarization is the process of consolidating a large amount of sentiments and opinions into a clear and brief statement for an easier grasp on the underlying context. Major summarization methods include, Extractive method, Sentence Ranking, Abstractive method and Clustering of Textual Segments. Hence it is important to judge and classify these reviews and present a laconic opinion so it would be easier for users to obtain a gist and overall polarity on the various reviews instead of going through all of them

Extractive Text Summarization Using Graph Based Ranking Algorithm And Mean Shift Clustering

Text summarization is the process of converting a huge text file into a summarized version preserving its meaning and context. The major aspect of any extractive text summarization technique is to provide precise and accurate summary by using any sentence ranking algorithms. The text is first tokenized and preprocessed. The sentences in the text are ranked according to the corresponding graph based ranking algorithm and finally the top K-scored sentences are included in the summary. Combining the graph based ranking algorithm along with mean shift clustering enables us to efficiently summarize the articles increasing the precision of the system generated summary and also reduces the execution time. The efficiency of these approaches are evaluated using rouge metrics and execution time.

Extractive Text Summarization using Deep Natural Language Fuzzy Processing

Text summarization is most trending research areas in a modern context. The main aim of this project is to reduce text size while preserving the information underlying into it. In summary construction level, in general, given complex task which are basically will involve with deep natural language fuzzy processing methodologies. In general, an extractive based summary method is the very simple original text of subset of which will not guarantee as best narrative coherence output, because they are most conveniently representing an approximate summarized content from given text-based only on relevance judgment. In an automatic process of fuzzy summarization which is divided into the following steps: Pre-processing (sentence segmentation, tokenization, stop words removal), Feature Extraction, Sentence Scoring, Sentence Ranking and Summary Extraction.