FastSum

Schilder, Frank; Kondadadi, Ravikumar

doi:10.3115/1557690.1557748

Cited by 37 publications

(5 citation statements)

References 4 publications

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“…In addition to that, a Support Vector Machine, or SVM, was utilized throughout the research (SVM). Schilder and Kondadadi (2008) look into using a support vector machine to grade the content of clusters for query-based multiple document synthesis. The research toolbox includes genetic algorithms as an additional instrument.…”

Section: Literature Surveymentioning

confidence: 99%

Extractive text summarization for biomedical transcripts using deep dense LSTM‐CNN framework

Bedi,

Bala,

Sharma

2023

Expert Systems

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The most recent and precise biological and healthcare knowledge is critical in the current outbreak such as COVID. In today's small world, everyone needs timely and appropriate medical information to prevent contagious diseases. Extraction of important information from medical conversations and dissemination to patients and doctors may benefit in the treatments of doctor tiredness and patient amnesia.ProblemAutomatic text summarizing is essential for gaining great knowledge in any topic in an efficient and productive manner. The material included in health records is vital to our understanding of kind illness and its manifestations. Creating a comprehensive and standard kind of content is becoming an unavoidable and crucial problem in the medical process as a result of the massive amounts of fragmented data created in many sectors.ApproachThe purpose of this study is to employ NLP‐based deep learning algorithms for text summary that perform well on linguistic text summarization data, and then modify/adapt these for biomedical domain‐specific text summarization. This paper provides an approach developed in‐house for condensing ill‐punctuated or unpunctuated discussion transcripts into more intelligible summaries, which combines topic modelling and phrase selection with punctuation restorations. For autonomous synthesis of medical reports from biomedical transcripts, this research proposes using an end‐to‐end summarization technique, Deep Dense Long Short Term Memory Network (LSTM), followed by Convolutional Neural Network (CNN).ResultsExtensive testing, examination, and comparing have demonstrated that this summarizer works well for medical transcript summarization. The suggested approach achieved an average ROUGE score of 93.5% using a single document summary. Furthermore, by comparing new techniques to previous ones, the utility and accuracy of novel strategies would be shown. The results reveal that models trained on ordinary language provide comparable results on a biomedical testing set, with one model outperforming the linguistic test set.

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Section: Literature Surveymentioning

confidence: 99%

Extractive text summarization for biomedical transcripts using deep dense LSTM‐CNN framework

Bedi,

Bala,

Sharma

2023

Expert Systems

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“…We could view our task as answering a query, "Why does the writer feel [emotion]?". However, such queries are more general than query-based summarization (Daumé III and Marcu, 2006;Otterbacher et al, 2009;Schilder and Kondadadi, 2008;Nema et al, 2017;Baumel et al, 2018;Laskar et al, 2020;Su et al, 2021;Zhong et al, 2021), where queries tend to be more document-specific. Perhaps a closer task is opinion summarization, or aspect-based summarization more generally.…”

Section: Related Workmentioning

confidence: 99%

Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts

Zhan¹,

Sosea²,

Caragea³

et al. 2022

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

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Crises such as the COVID-19 pandemic continuously threaten our world and emotionally affect billions of people worldwide in distinct ways. Understanding the triggers leading to people's emotions is of crucial importance. Social media posts can be a good source of such analysis, yet these texts tend to be charged with multiple emotions, with triggers scattering across multiple sentences. This paper takes a novel angle, namely, emotion detection and trigger summarization, aiming to both detect perceived emotions in text, and summarize events and their appraisals that trigger each emotion. To support this goal, we introduce COVIDET (Emotions and their Triggers during Covid-19), a dataset of ~1, 900 English Reddit posts related to COVID-19, which contains manual annotations of perceived emotions and abstractive summaries of their triggers described in the post. We develop strong baselines to jointly detect emotions and summarize emotion triggers. Our analyses show that COVIDET presents new challenges in emotion-specific summarization, as well as multi-emotion detection in long social media posts.* Hongli Zhan and Tiberiu Sosea contributed equally. Reddit Post1: My sibling is 19 and she constantly goes places with her friends and to there houses and its honestly stressing me out. 2: Our grandfather lives with us and he has dementia along with other health issues and my mom has diabetes and heart problems and I have autoimmune diseases & chronic health issues. 3: She also has asthma. 4: Its stressing me out because despite this she seems to not care about how badly it would affect all of us if we were to get the virus. 5: And sadly I feel like its not much I can do she literally doesn't respect my mom and though I'm older she doesn't respect me either. 6: Its so frustrating.

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“…It is also possible to distinguish between generic summaries and query-focused summaries (also known as user-focused or topic-focused). Examples of approaches that generate these types of summaries can be found in Schilder and Kondadadi (2008) or Zhao, Wu and Huang (2009). The first type of summaries can serve as a surrogate of the original text, as these may try to represent all relevant facts of the source text.…”

Section: Related Workmentioning

confidence: 99%

“…Besides the common features based on keywords and sentence position, a new set of features based on Wikipedia 1 and query logs are also used in a way that, for example, sentences containing query terms or Wikipedia entities are indicative of important content. In Schilder and Kondadadi (2008), a query-focused multi-document summariser is presented, named as FastSum, where sentences are ranked using a machine-learning technique called Support Vector Regression (SVR), and Least Angle Regression for feature selection. SVR was used in summarisation before, in the approach described in Li et al .…”

Section: Related Workmentioning

confidence: 99%

COMPENDIUM: a text summarisation tool for generating summaries of multiple purposes, domains, and genres

Lloret¹,

Palomar²

2012

Nat. Lang. Eng.

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In this paper, we present a Text Summarisation tool, compendium, capable of generating the most common types of summaries. Regarding the input, single-and multi-document summaries can be produced; as the output, the summaries can be extractive or abstractiveoriented; and finally, concerning their purpose, the summaries can be generic, query-focused, or sentiment-based. The proposed architecture for compendium is divided in various stages, making a distinction between core and additional stages. The former constitute the backbone of the tool and are common for the generation of any type of summary, whereas the latter are used for enhancing the capabilities of the tool. The main contributions of compendium with respect to the state-of-the-art summarisation systems are that (i) it specifically deals with the problem of redundancy, by means of textual entailment; (ii) it combines statistical and cognitive-based techniques for determining relevant content; and (iii) it proposes an abstractive-oriented approach for facing the challenge of abstractive summarisation. The evaluation performed in different domains and textual genres, comprising traditional texts, as well as texts extracted from the Web 2.0, shows that compendium is very competitive and appropriate to be used as a tool for generating summaries.

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FastSum

Cited by 37 publications

References 4 publications

Extractive text summarization for biomedical transcripts using deep dense LSTM‐CNN framework

Extractive text summarization for biomedical transcripts using deep dense LSTM‐CNN framework

Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts

COMPENDIUM: a text summarisation tool for generating summaries of multiple purposes, domains, and genres

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