text: 258,92,1504266340072407043,1352219823539957762,Liam Dugan,"Ever wanted to automatically generate flashcards from a textbook PDF? Our new paper “A Feasibility Study of Answer-Unaware Question Generation for Education” investigates how feasible this is given recent advancements! <LINK> Thread👇 <LINK> Fully automatic generation of quizzes or flashcards necessitates “answer-unaware” QG models (i.e. ones that don't require manual selection of answer spans). These models have to decide what is and isn’t relevant to ask. This is non-trivial! (1/5) <LINK> Unsurprisingly, these QG models (typically trained on datasets like SQuAD) tend to ask about topics that ""annotators are most likely to pick"". These topics are not necessarily the most educationally salient. Summarization helps solve this problem! (2/5) Running QG on auto-summarized text allows us to restrict the model to only see sentences that are highly salient to the larger passage. This improves relevance of generated questions (61% -&gt; 78%). The effect is even more pronounced with human-summarized text (61% -&gt; 95%). (3/5) <LINK> Also, since summaries tend to consist of simpler, more self-contained sentences, QG on summaries produces questions that tend to be more interpretable out of context (56% -&gt; 74%). Again, this effect is even larger when using human-written summaries as input (56% -&gt; 94%). (4/5) These two factors lead to large increases in acceptability of generated Qs on summaries (83%) vs original text (33%) with no corresponding drop in bolded key-term coverage.  tl;dr Automatic flashcard/quiz generation *is* currently feasible with min. supervision🎉 (5/5) <LINK> Don’t believe the results? Run our repo and try it out for yourself!  <LINK>  We include a joint summarization and QG pipeline interface as well as .txt files of textbook chapters. We also provide our data and the code to reproduce our analysis. @abhisuri97 Oh you bet it can! Currently only takes .txt files as input but I you can pretty easily hook up some PDF extraction to run it on PDFs 😁",https://arxiv.org/abs/2203.08685,"We conduct a feasibility study into the applicability of answer-agnostic question generation models to textbook passages. We show that a significant portion of errors in such systems arise from asking irrelevant or uninterpretable questions and that such errors can be ameliorated by providing summarized input. We find that giving these models human-written summaries instead of the original text results in a significant increase in acceptability of generated questions (33% $\rightarrow$ 83%) as determined by expert annotators. We also find that, in the absence of human-written summaries, automatic summarization can serve as a good middle ground. ",A Feasibility Study of Answer-Agnostic Question Generation for Education,8,"['Ever wanted to automatically generate flashcards from a textbook PDF? Our new paper “A Feasibility Study of Answer-Unaware Question Generation for Education” investigates how feasible this is given recent advancements!\n\n<LINK>\n\nThread👇 <LINK>', ""Fully automatic generation of quizzes or flashcards necessitates “answer-unaware” QG models (i.e. ones that don't require manual selection of answer spans). These models have to decide what is and isn’t relevant to ask. This is non-trivial! (1/5) https://t.co/IG0kaTHPTw"", 'Unsurprisingly, these QG models (typically trained on datasets like SQuAD) tend to ask about topics that ""annotators are most likely to pick"". These topics are not necessarily the most educationally salient. Summarization helps solve this problem! (2/5)', 'Running QG on auto-summarized text allows us to restrict the model to only see sentences that are highly salient to the larger passage. This improves relevance of generated questions (61% -&gt; 78%). The effect is even more pronounced with human-summarized text (61% -&gt; 95%). (3/5) https://t.co/H7VIwZ0bzu', 'Also, since summaries tend to consist of simpler, more self-contained sentences, QG on summaries produces questions that tend to be more interpretable out of context (56% -&gt; 74%). Again, this effect is even larger when using human-written summaries as input (56% -&gt; 94%). (4/5)', 'These two factors lead to large increases in acceptability of generated Qs on summaries (83%) vs original text (33%) with no corresponding drop in bolded key-term coverage. \n\ntl;dr Automatic flashcard/quiz generation *is* currently feasible with min. supervision🎉 (5/5) https://t.co/pLcf4i2Aq4', 'Don’t believe the results? Run our repo and try it out for yourself! \n\nhttps://t.co/OimFWV9D4U \n\nWe include a joint summarization and QG pipeline interface as well as .txt files of textbook chapters. We also provide our data and the code to reproduce our analysis.', '@abhisuri97 Oh you bet it can! Currently only takes .txt files as input but I you can pretty easily hook up some PDF extraction to run it on PDFs 😁']",22,03,1994