Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision

Current medical question answering systems have difficulty processing long, detailed and informally worded questions submitted by patients, called Consumer Health Questions (CHQs). To address this issue, we introduce a medical question understanding and answering system with knowledge grounding and semantic self-supervision. Our system is a pipeline that first summarizes a long, medical, user-written question, using a supervised summarization loss. Then, our system performs a two-step retrieval to return answers. The system first matches the summarized user question with an FAQ from a trusted medical knowledge base, and then retrieves a fixed number of relevant sentences from the corresponding answer document. In the absence of labels for question matching or answer relevance, we design 3 novel, self-supervised and semantically-guided losses. We evaluate our model against two strong retrieval-based question answering baselines. Evaluators ask their own questions and rate the answers retrieved by our baselines and own system according to their relevance. They find that our system retrieves more relevant answers, while achieving speeds 20 times faster. Our self-supervised losses also help the summarizer achieve higher scores in ROUGE, as well as in human evaluation metrics. We release our code to encourage further research.Comment: Accepted as Main Conference Long paper at COLING 202

Evaluating Web Search Result Summaries

The aim of our research is to produce and assess short summaries to aid users’ relevance judgements, for example for a search engine result page. In this paper we present our new metric for measuring summary quality based on representativeness and judgeability, and compare the summary quality of our system to that of Google. We discuss the basis for constructing our evaluation methodology in contrast to previous relevant open evaluations, arguing that the elements which make up an evaluation methodology: the tasks, data and metrics, are interdependent and the way in which they are combined is critical to the effectiveness of the methodology. The paper discusses the relationship between these three factors as implemented in our own work, as well as in SUMMAC/MUC/DUC

Document Summarization Using NMF and Pseudo Relevance Feedback Based on K-Means Clustering

According to the increment of accessible text data source on the internet, it has increased the necessity of the automatic text document summarization. However, the performance of the automatic methods might be poor because the semantic gap between high level user's summary requirement and low level vector representation of machine exists. In this paper, to overcome that problem, we propose a new document summarization method using a pseudo relevance feedback based on clustering method and NMF (non-negative matrix factorization). Relevance feedback is effective technique to minimize the semantic gap of information processing, but the general relevance feedback needs an intervention of a user. Additionally, the refined query without user interference by pseudo relevance feedback may be biased. The proposed method provides an automatic relevance judgment to reformulate query using the clustering method for minimizing a bias of query expansion. The method also can improve the quality of document summarization since the summarized documents are influenced by the semantic features of documents and the expanded query. The experimental results demonstrate that the proposed method achieves better performance than the other document summarization methods

Multi-document text summarization using text clustering for Arabic Language

The process of multi-document summarization is producing a single summary of a collection of related documents. In this work we focus on generic extractive Arabic multi-document summarizers. We also describe the cluster approach for multi-document summarization. The problem with multi-document text summarization is redundancy of sentences, and thus, redundancy must be eliminated to ensure coherence, and improve readability. Hence, we set out the main objective as to examine multi-document summarization salient information for text Arabic summarization task with noisy and redundancy information. In this research we used Essex Arabic Summaries Corpus (EASC) as data to test and achieve our main objective and of course its subsequent subobjectives. We used the token process to split the original text into words, and then removed all the stop words, and then we extract the root of each word, and then represented the text as bag of words by TFIDF without the noisy information. In the second step we applied the K-means algorithm with cosine similarity in our experimental to select the best cluster based on cluster ordering by distance performance. We applied SVM to order the sentences after selected the best cluster, then we selected the highest weight sentences for the final summary to reduce redundancy information. Finally, the final summary results for the ten categories of related documents are evaluated using Recall and Precision with the best Recall achieved is 0.6 and Precision is 0.6

Generating Question-Answer Hierarchies

The process of knowledge acquisition can be viewed as a question-answer game between a student and a teacher in which the student typically starts by asking broad, open-ended questions before drilling down into specifics (Hintikka, 1981; Hakkarainen and Sintonen, 2002). This pedagogical perspective motivates a new way of representing documents. In this paper, we present SQUASH (Specificity-controlled Question-Answer Hierarchies), a novel and challenging text generation task that converts an input document into a hierarchy of question-answer pairs. Users can click on high-level questions (e.g., "Why did Frodo leave the Fellowship?") to reveal related but more specific questions (e.g., "Who did Frodo leave with?"). Using a question taxonomy loosely based on Lehnert (1978), we classify questions in existing reading comprehension datasets as either "general" or "specific". We then use these labels as input to a pipelined system centered around a conditional neural language model. We extensively evaluate the quality of the generated QA hierarchies through crowdsourced experiments and report strong empirical results.Comment: ACL camera ready + technical note on pipeline modifications for demo (15 pages