Summaries of Texts

Every day, the amount of textual data created increases exponentially, both in terms of complexity and volume. Massive amounts of information are generated by social media, news articles, emails, text messages and other resources, making it difficult to read lengthy language materials. Our main objective in the paper is to obtain a short understandable and fluent abstractive summary of any given text. The Abstractive Text Summarizer automatically gives the summary of the text by generating new phrase, rephrasing or using the new words which are not present in the original text. In this paper, a machine learning architecture i.e. Stacked LSTM based on attention mechanism using Sequence-to-Sequence model is proposed, to generate the summary using abstractive approach for Amazon reviews of fine foods dataset. Our approach allows the model to accept content and provide a concise summary that may clearly describe the gist of the original text. The experiments on Amazon reviews of fine foods dataset show that our model obtained BLEU Score as 0.91 for a test set.

This study compared young and older adults' summaries of expository texts under the hypothesis that older adults would be more experienced, hence more accurate, at summarizing texts. Two types of expository texts were used: procedural and descriptive texts. The texts were read by a panel of judges who wrote summaries of each text and identified the central ideas of each text; the judges' summaries were used to prepare lists of the central ideas of each text and to write standard summaries of each text. The participants read four texts, two orally and two silently, and then wrote summaries, which were limited to 50 words. Words-per-minute reading times were collected and the summaries were scored on two measures of content, how many ideas were reproduced from the original texts and the proportion of central ideas that were reproduced, and two measures of length, the number of sentences and the number of words. Although the older adults read more slowly than the young adults, the older adults reproduced more total and central ideas than the young ideas.

Many researches have been converging on automatic text summarization as increasing of text documents due to the expansion of information diffusion constantly. The objective of this proposal is to achieve the most reliable and substantial context or most relevant brief summary of the text in extractive manner. The extractive text summarization produces the short summary of a certain text which contains the most important information of original text by extracting the set of sentences from the original document. This paper proposes an improved extractive text summarization method for documents by enhancing the conventional lexical chain method to produce better relevant information of the text using three distinct features or characteristics of keyword in a text. The keyword of the document is labeled using our previous work, transition probability distribution generator model which can learn the characteristics of the keyword in a document, and generates their probability distribution upon each feature.

We introduce a technique for creating novel, enhanced thumbnails of Web pages. These thumbnails combine the advantages of plain thumbnails and text summaries to provide consistent performance on a variety of tasks. We conducted a study in which participants used three different types of summaries (enhanced thumbnails, plain thumbnails, and text summaries) to search Web pages to find several different types of information. Participants took an average of 67, 86, and 95 seconds to find the answer with enhanced thumbnails, plain thumbnails, and text summaries, respectively. As expected, there was a strong effect of question category. For some questions, text summaries outperformed plain thumbnails, while for other questions, plain thumbnails outperformed text summaries. Enhanced thumbnails (which combine the features of text summaries and plain thumbnails) had more consistent performance than either text summaries or plain thumbnails, having for all categories the best performance or performance that was statistically indistinguishable from the best.

In this paper we address the Sentiment Analysis problem from the end user's perspective. An end user might desire an automated at-a-glance presentation of the main points made in a single review or how opinion changes time to time over multiple documents. To meet the requirement we propose a relatively generic opinion 5Ws structurization, further used for textual and visual summary and tracking. The 5W task seeks to extract the semantic constituents in a natural language sentence by distilling it into the answers to the 5W questions: Who, What, When, and The visualization system facilitates users to generate sentiment tracking with textual summary and sentiment polarity wise graph based on any dimension or combination of dimensions as they want i.e. Who are the actors and What are their sentiment regarding any topic, changes in sentiment during When and Where and the reasons for change in sentiment as Why.

We investigated the efficacy of visual and textual web page previews in predicting the helpfulness of web pages related to a specific topic. We ran two studies in the usability lab and collected data through an online survey. Participants (total of 245) were asked to rate the expected helpfulness of a web page based on a preview (four different thumbnail variations: a textual web page summary, a thumbnail/title/URL combination, a title/URL combination). In the lab studies, the same participants also rated the helpfulness of the actual web pages themselves. In the online study, the web page ratings were collected from a separate group of participants. Our results show that thumbnails add information about the relevance of web pages that is not available in the textual summaries of web pages (title, snippet & URL). However, showing only thumbnails, with no textual information, results in poorer performance than showing only textual summaries. The prediction inaccuracy caused by textual vs. visual previews was different: textual previews tended to make users overestimate the helpfulness of web pages, whereas thumbnails made users underestimate the helpfulness of web pages in most cases. In our study, the best performance was obtained by combining sufficiently large thumbnails (at least 200x200 pixels) with page titles and URLs - and it was better to make users focus primarily on the thumbnail by placing the title and URL below the thumbnail. Our studies highlighted four key aspects that affect the performance of previews: the visual/textual mode of the previews, the zoom level and size of the thumbnail, as well as the positioning of key information elements.

Previous work on review summarization focused on measuring the sentiment toward the main aspects of the reviewed product or business, or on creating a textual summary. These approaches provide only a partial view of the data: aspect-based sentiment summaries lack sufficient explanation or justification for the aspect rating, while textual summaries do not quantify the significance of each element, and are not well-suited for representing conflicting views. Recently, Key Point Analysis (KPA) has been proposed as a summarization framework that provides both textual and quantitative summary of the main points in the data. We adapt KPA to review data by introducing Collective Key Point Mining for better key point extraction; integrating sentiment analysis into KPA; identifying good key point candidates for review summaries; and leveraging the massive amount of available reviews and their metadata. We show empirically that these novel extensions of KPA substantially improve its performance. We demonstrate that promising results can be achieved without any domain-specific annotation, while human supervision can lead to further improvement.

In this paper we address the Sentiment Analysis problem from the end user’s perspective. An end user might desire an automated at-a-glance presentation of the main points made in a single review or how opinion changes time to time over multiple documents. To meet the requirement we propose a relatively generic opinion 5Ws structurization, further used for textual and visual summary and tracking. The 5W task seeks to extract the semantic constituents in a natural language sentence by distilling it into the answers to the 5W questions: Who, What, When, Where and Why. The visualization system facilitates users to generate sentiment tracking with textual summary and sentiment polarity wise graph based on any dimension or combination of dimensions as they want i.e. “Who” are the actors and “What” are their sentiment regarding any topic, changes in sentiment during “When” and “Where” and the reasons for change in sentiment as “Why”.

In this paper, we address the problem of generating both visual and textual summaries for tagged image collections simultaneously. The visual and textual summaries consist of representative images and tags of the collection, which are selected through a proposed cross-media voting scheme. In the voting scheme, the likelihood of an image to be a representative is voted by not only other images but also the tags, according to the intra-media and cross-media affinities. The likelihood of a tag to be a representative is obtained in similar manner at the same time. We demonstrate that the proposed scheme produces more informative textual and visual summaries than summarizing images and tags separately.

Text summary is an information processing technology that aims to extract the important information in the text and filter out the useless information. In the research literature, text summary methods generate a text summary by clustering, supervised-based, and unsupervised-based methods. However, the K value selection of K-means clustering algorithms is manually specified, and the improper selection of the K value will lead to a poor clustering effect. At the same time, most automatic text summary methods have high redundancy. To solve the above problems, this paper proposes an automatic text summary method based on an optimized K-means clustering algorithm with symmetry and the Maximal-Marginal-Relevance (MMR) algorithm. This method uses the Genetic Algorithm with symmetry to optimize the K value selection of the K-means clustering algorithm and reduces the sentence redundancy of the text summary by using the Maximal-Marginal-Relevance algorithm. The experimental results show that the three evaluation indicators of the proposed method, namely, ROUGE-1, ROUGE-2, and ROUGE-L, have increased by an average of 96.81%, 2.39 times, and 10.15%, respectively, compared with the other three automatic text summary methods, including Lead-3, Text-Rank, and KM-MMR. In conclusion, the proposed method in this paper can obtain better-quality text summaries.

We introduce a technique for creating novel, textually-enhanced thumbnails of Web pages. These thumbnails combine the advantages of image thumbnails and text summaries to provide consistent performance on a variety of tasks. We conducted a study in which participants used three different types of summaries (enhanced thumbnails, plain thumbnails, and text summaries) to search Web pages to find several different types of information. Participants took an average of 67, 86, and 95 seconds to find the answer with enhanced thumbnails, plain thumbnails, and text summaries, respectively. We found a strong effect of question category. For some questions, text outperformed plain thumbnails, while for other questions, plain thumbnails outperformed text. Enhanced thumbnails (which combine the features of text summaries and plain thumbnails) were more consistent than either text summaries or plain thumbnails, having for all categories the best performance or performance that was statistically indistinguishable from the best.

Quality of written summary texts: An analysis in the context of gender and school variables

Video summarization aims to create short, accurate, and cohesive summaries of longer videos. Despite the existence of various video summarization datasets, a notable limitation is their limited amount of source videos, which hampers the effective training of advanced large vision-language models (VLMs). Additionally, most existing datasets are created for video-to-video summarization, overlooking the contemporary need for multimodal video content summarization. Recent efforts have been made to expand from unimodal to multimodal video summarization, categorizing the task into three sub-tasks based on the summary's modality: video-to-video (V2V), video-to-text (V2T), and a combination of video and text summarization (V2VT). However, the textual summaries in previous multimodal datasets are inadequate. To address these issues, we introduce Instruct-V2Xum, a cross-modal video summarization dataset featuring 30,000 diverse videos sourced from YouTube, with lengths ranging from 40 to 940 seconds and an average summarization ratio of 16.39%. Each video summary in Instruct-V2Xum is paired with a textual summary that references specific frame indexes, facilitating the generation of aligned video and textual summaries. In addition, we propose a new video summarization framework named V2Xum-LLM. V2Xum-LLM, specifically V2Xum-LLaMA in this study, is the first framework that unifies different video summarization tasks into one large language model's (LLM) text decoder and achieves task-controllable video summarization with temporal prompts and task instructions. Experiments show that V2Xum-LLaMA outperforms strong baseline models on multiple video summarization tasks. Furthermore, we propose an enhanced evaluation metric for V2V and V2VT summarization tasks.

Graphical Abstracts for Research Papers: Why You Need One and How to Create It

Abstract—in today's rapidly growing information age, text summary has become a critical and important instrument for help understanding text information. it is really hard for human beings to physically summarize huge textual documents also there is an abundance of text content available online. text summarization is an active research field that works on compressing large pieces of text into smaller texts that preserve relevant information. text summary classified as extractive or abstractive. methods of extractive summarization working by deciding important text sentences and choosing them as a summary. that method based only on sentences from the source text. methods of abstractive summarization aim to paraphrase important information in a new form like that of humans. text summary can be achieved using different deep learning techniques, such as: fuzzy logic, Convolutional Neural (CNN), transformers, neural network, reinforcement learning, etc. in the past three years, the research trend in text summarization has also undergone a slight change, where new trends have appeared that are trends that lead to enhancement, how to improve the efficiency of text summarization to obtain high accuracy. we have made several attempts in this paper to discuss the various techniques used on the basis of deep learning for text summary in these years and observe the new trends in the field of deep learning.