Summarizing a document requires identifying the important parts of the document with an objective of providing a quick overview to a reader. However, a long article can span several topics and a single summary cannot do justice to all the topics. Further, the interests of readers can vary and the notion of importance can change across them. Existing summarization algorithms generate a single summary and are not capable of generating multiple summaries tuned to the interests of the readers. In this paper, we propose an attention based RNN framework to generate multiple summaries of a single document tuned to different topics of interest. Our method outperforms existing baselines and our results suggest that the attention of generative networks can be successfully biased to look at sentences relevant to a topic and effectively used to generate topic-tuned summaries.
Style transfer deals with the algorithms to transfer the stylistic properties of a piece of text into that of another while ensuring that the core content is preserved. There has been a lot of interest in the field of text style transfer due to its wide application to tailored text generation. Existing works evaluate the style transfer models based on content preservation and transfer strength. In this work, we propose a reinforcement learning based framework that directly rewards the framework on these target metrics yielding a better transfer of the target style. We show the improved performance of our proposed framework based on automatic and human evaluation on three independent tasks: wherein we transfer the style of text from formal to informal, high excitement to low excitement, modern English to Shakespearean English, and vice-versa in all the three cases. Improved performance of the proposed framework over existing state-of-the-art frameworks indicates the viability of the approach.
Human activity prediction is an interesting problem with a wide variety of applications like intelligent virtual assistants, contextual marketing, etc. One formulation of this problem is jointly predicting human activities (viz. eating, commuting, etc.) with associated durations. Herein a deep learning system is proposed for this problem. Given a sequence of past activities and durations, the system estimates the probabilities for future activities and their durations. Two distinct Long-Short Term Memory (LSTM) networks are developed that cater to different assumptions about the data and achieve different modeling complexities and prediction accuracies. The networks are trained and tested with two real-world datasets, one being publicly available while the other collected from a field experiment. Modeling on the segment level public dataset mitigates the cold-start problem. Experiments indicate that compared to traditional approaches based on sequence mining or hidden Markov modeling, LSTM networks perform significantly better. The ability of LSTM networks to detect long term correlations in activity data is also demonstrated. The trained models are each less than 500KB in size and can be deployed to run in real-time on a mobile device without any dependencies on the cloud. This can help applications like mobile personal assistants by providing predictive context.
Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes.While invaluable to clinicians and researchers, creating digital SOAP notes is burdensome, contributing to physician burnout. In this paper, we introduce the first complete pipelines to leverage deep summarization models to generate these notes based on transcripts of conversations between physicians and patients. After exploring a spectrum of methods across the extractive-abstractive spectrum, we propose CLUSTER2SENT, an algorithm that (i) extracts important utterances relevant to each summary section; (ii) clusters together related utterances; and then (iii) generates one summary sentence per cluster. CLUSTER2SENT outperforms its purely abstractive counterpart by 8 ROUGE-1 points, and produces significantly more factual and coherent sentences as assessed by expert human evaluators. For reproducibility, we demonstrate similar benefits on the publicly available AMI dataset. Our results speak to the benefits of structuring summaries into sections and annotating supporting evidence when constructing summarization corpora. ... DR: So are you taking the Monteluekast regularly? PT: Yeah, one everyday like you said. DR: Good. And is it helping? Do you have chest pains anymore? PT: No. No chest pains. DR:That's good. PT: Although I do still have some cough. DR: I see. And do you get, like, mucous with it or is it dry? PT: Umm no it's usually dry. No mucous. ... SUBEJCTIVE Chief Complaint -Post viral respiratory allergy Review of systems -Denies chest pain. confirms dry cough. Medications -Monteleukast. Fluticasone OBJECTIVE Lab results -ASSESSMENT Assessment -Patient feeling better after taking inhaler. Still has some cough but no chest pain. PLAN Diagnostics and Appointments -Followup in 1 week to assess condition and decide when to stop using the inhaler. DR: Good. And is it helping? Do you have chest pains anymore? PT: No. No chest pains PT: Although I do still have some cough. DR: I see. And do you get, like, mucous with it or is it dry? PT: Umm no it's usually dry. No mucous. DR: So are you taking the Monteluekast regularly? PT: Yeah, one everyday like you said.
Pretraining techniques leveraging enormous datasets have driven recent advances in text summarization. While folk explanations suggest that knowledge transfer accounts for pretraining's benefits, little is known about why it works or what makes a pretraining task or dataset suitable. In this paper, we challenge the knowledge transfer story, showing that pretraining on documents consisting of character n-grams selected at random, we can nearly match the performance of models pretrained on real corpora. This work holds the promise of eliminating upstream corpora, which may alleviate some concerns over offensive language, bias, and copyright issues. To see whether the small residual benefit of using real data could be accounted for by the structure of the pretraining task, we design several tasks motivated by a qualitative study of summarization corpora. However, these tasks confer no appreciable benefit, leaving open the possibility of a small role for knowledge transfer. 1
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