Podcast
Questions and Answers
What was tuned while optimizing for R-1 in the experiments conducted?
What was tuned while optimizing for R-1 in the experiments conducted?
How many domain pairs were experimented on with the total of 6 categories?
How many domain pairs were experimented on with the total of 6 categories?
What was the word embedding size increased to from its default value?
What was the word embedding size increased to from its default value?
What could potentially undermine the generalizability of the conclusions drawn from the experiments?
What could potentially undermine the generalizability of the conclusions drawn from the experiments?
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Which method was compared against N EURAL S UM in the results?
Which method was compared against N EURAL S UM in the results?
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What is the primary metric used to evaluate the models in the experiments?
What is the primary metric used to evaluate the models in the experiments?
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What pre-trained embedding technique was used to initialize the word embedding?
What pre-trained embedding technique was used to initialize the word embedding?
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What approach was taken regarding hyperparameter tuning during the out-of-domain experiments?
What approach was taken regarding hyperparameter tuning during the out-of-domain experiments?
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What method was used as a baseline for comparison in the summarization results?
What method was used as a baseline for comparison in the summarization results?
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What does the acronym TF stand for in the context of text summarization?
What does the acronym TF stand for in the context of text summarization?
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What was the reason for initializing with FAST T EXT pre-trained embedding?
What was the reason for initializing with FAST T EXT pre-trained embedding?
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What aspect of the results indicated a need for improvement in the methodology?
What aspect of the results indicated a need for improvement in the methodology?
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How many sentences were extracted as a summary based on exploratory analysis?
How many sentences were extracted as a summary based on exploratory analysis?
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What complicates the collection of in-domain datasets for low-resource languages like Indonesian?
What complicates the collection of in-domain datasets for low-resource languages like Indonesian?
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Which of the following methods is described as extractive?
Which of the following methods is described as extractive?
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What is a potential reason that initializing with FAST T EXT slightly lowers scores?
What is a potential reason that initializing with FAST T EXT slightly lowers scores?
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Which summarization method consistently outperforms the L EAD -3 baseline in almost all scenarios?
Which summarization method consistently outperforms the L EAD -3 baseline in almost all scenarios?
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What is indicated as the upper bound extractive summarizer in the study?
What is indicated as the upper bound extractive summarizer in the study?
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Which word embedding size yields the best results for N EURAL S UM?
Which word embedding size yields the best results for N EURAL S UM?
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What trend is observed regarding training on out-of-domain data compared to in-domain data?
What trend is observed regarding training on out-of-domain data compared to in-domain data?
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Which method performs slightly lower than L EAD -3 but is still competitive in its results?
Which method performs slightly lower than L EAD -3 but is still competitive in its results?
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Why does training on Headline data yield the best results for many target domains?
Why does training on Headline data yield the best results for many target domains?
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What element of the models is generally computed over 5 folds?
What element of the models is generally computed over 5 folds?
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Which of these methods is noted as an unsupervised model in the comparison?
Which of these methods is noted as an unsupervised model in the comparison?
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What is the primary evaluation metric used for text summarization in the study?
What is the primary evaluation metric used for text summarization in the study?
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What advantage is noted regarding training on out-of-domain corpora?
What advantage is noted regarding training on out-of-domain corpora?
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What does the study indicate about the performance of the best model in relation to ROUGE scores?
What does the study indicate about the performance of the best model in relation to ROUGE scores?
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What is the size of the dataset used in this summarization study?
What is the size of the dataset used in this summarization study?
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Which potential focus for future work is suggested in the study?
Which potential focus for future work is suggested in the study?
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What type of summarization approach does SummaRuNNer employ?
What type of summarization approach does SummaRuNNer employ?
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Which model is recognized for its use of pointer-generator networks in summarization?
Which model is recognized for its use of pointer-generator networks in summarization?
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Which paper explores neural attention mechanisms for sentence summarization?
Which paper explores neural attention mechanisms for sentence summarization?
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What main focus does the paper by Nenkova and Vanderwende address concerning summarization?
What main focus does the paper by Nenkova and Vanderwende address concerning summarization?
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In which conference was the paper discussing 'Neural summarization by extracting sentences and words' presented?
In which conference was the paper discussing 'Neural summarization by extracting sentences and words' presented?
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What is a common theme shared by the works of Rush, Chopra, and Weston, as well as Nallapati, Zhou, and Santos?
What is a common theme shared by the works of Rush, Chopra, and Weston, as well as Nallapati, Zhou, and Santos?
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Which technique is specifically mentioned as being central to the work of Paulus, Xiong, and R?
Which technique is specifically mentioned as being central to the work of Paulus, Xiong, and R?
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Which authors contributed to research on the impact of frequency in summarization?
Which authors contributed to research on the impact of frequency in summarization?
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Study Notes
Neural Extractive Summarization
- The authors use a Neural Extractive Summarization model.
- The model is trained on Indonesian news articles.
- The model is evaluated using ROUGE-1, ROUGE-2 and ROUGE-L metrics.
- Neural Extractive Summarization outperforms other models such as LEAD-3, LEXRANK and BAYES.
- The performance of the model is significantly lower than the theoretical upper bound, suggesting the dataset is challenging.
Out-of-Domain Performance
- The authors evaluate the model's performance in out-of-domain scenarios.
- The model is trained on articles from one category and evaluated on articles from a different category.
- The model outperforms LEAD-3 and LEXRANK in out-of-domain scenarios.
- Performance of the model is surprisingly better in out-of-domain scenarios compared to in-domain scenarios.
Key Findings
- The dataset of 19,000 article-summary pairs is publicly available.
- The study uses the pre-trained FASTTEXT embedding for Indonesian.
- Pre-trained embedding slightly lowers the scores but remains within one standard deviation.
- The authors suggest using the model for new use cases for which data is limited.
- The authors acknowledge the support from Shortir and Tempo.
- The authors express gratitude for the contributions of anonymous reviewers and colleagues in the development of the research.
- The authors recommend further exploration of newer neural models like SummaRuNNer and incorporating side information for improvements.
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Description
This quiz covers the use of Neural Extractive Summarization models trained on Indonesian news articles, evaluating their performance using ROUGE metrics. It explores out-of-domain performance and key findings related to the dataset and embedding techniques used. Test your understanding of these advanced summarization techniques and their applications.
