This study examines the potential of integrating Learning-to-Rank (LTR) with Query-focused Summarization (QFS) to enhance the summary relevance via content prioritization.Using a shared secondary decoder with the summarization decoder, we carry out the LTR task at the segment level.Compared to the state-of-the-art, our model outperforms on QMSum benchmark (all metrics) and matches on SQuALITY benchmark (2 metrics) as measured by Rouge and BertScore while offering a lower training overhead.Specifically, on the QMSum benchmark, our proposed system achieves improvements, particularly in Rouge-L (+0.42) and BertScore (+0.34), indicating enhanced understanding and relevance.While facing minor challenges in Rouge-1 and Rouge-2 scores on the SQuAL-ITY benchmark, the model significantly excels in Rouge-L (+1.47), underscoring its capability to generate coherent summaries.Human evaluations emphasize the efficacy of our method in terms of relevance and faithfulness of the generated summaries, without sacrificing fluency.A deeper analysis reveals our model's superiority over the state-of-the-art for broad queries, as opposed to specific ones, from a qualitative standpoint.We further present an error analysis of our model, pinpointing challenges faced and suggesting potential directions for future research in this field.

Automating the process of clinical text summarization could save clinicians' reading time and reduce their fatigue, acknowledging the necessity of human professionals in the loop. This paper addresses clinical text summarization, aiming to incorporate ontology concept relationships via a Graph Neural Network (GNN) into the summarization process. Specifically, we propose a model, extending Bart's encoder-decoder framework with GNN encoder and multi-head attentional layers for decoder, producing ontology-aware summaries. This GNN interacts with the textual encoder, influencing their mutual representations. The model's effectiveness is validated on two real-world radiology datasets. We also present an ablation study to elucidate the impact of varied graph configurations and an error analysis aimed at pinpointing potential areas for future improvements.

Mental health remains a significant challenge of public health worldwide. With increasing popularity of online platforms, many use the platforms to share their mental health conditions, express their feelings, and seek help from the community and counselors. Some of these platforms, such as Reachout, are dedicated forums where the users register to seek help. Others such as Reddit provide subreddits where the users publicly but anonymously post their mental health distress. Although posts are of varying length, it is beneficial to provide a short, but informative summary for fast processing by the counselors. To facilitate research in summarization of mental health online posts, we introduce Mental Health Summarization dataset, MentSum, containing over 24k carefully selected user posts from Reddit, along with their short user-written summary (called TLDR) in English from 43 mental health subreddits. This domain-specific dataset could be of interest not only for generating short summaries on Reddit, but also for generating summaries of posts on the dedicated mental health forums such as Reachout. We further evaluate both extractive and abstractive state-of-the-art summarization baselines in terms of Rouge scores, and finally conduct an in-depth human evaluation study of both user-written and system-generated summaries, highlighting challenges in this research.

Many scientific papers such as those in arXiv and PubMed data collections have abstracts with varying lengths of 50-1000 words and average length of approximately 200 words, where longer abstracts typically convey more information about the source paper.Up to recently, scientific summarization research has typically focused on generating short, abstractlike summaries following the existing datasets used for scientific summarization.In domains where the source text is relatively long-form, such as in scientific documents, such summary is not able to go beyond the general and coarse overview and provide salient information from the source document.The recent interest to tackle this problem motivated curation of scientific datasets, arXiv-Long and PubMed-Long, containing human-written summaries of 400-600 words, hence, providing a venue for research in generating long/extended summaries.Extended summaries facilitate a faster read while providing details beyond coarse information.In this paper, we propose TSTR, an extractive summarizer that utilizes the introductory information of documents as pointers to their salient information.The evaluations on two existing large-scale extended summarization datasets indicate statistically significant improvement in terms of ROUGE and average ROUGE (F1) scores (except in one case) as compared to strong baselines and state-of-theart.Comprehensive human evaluations favor our generated extended summaries in terms of cohesion and completeness.

Automatically generating accurate summaries from clinical reports could save a clinician's time, improve summary coverage, and reduce errors. We propose a sequence-to-sequence abstractive summarization model augmented with domain-specific ontological information to enhance content selection and summary generation. We apply our method to a dataset of radiology reports and show that it significantly outperforms the current state-of-the-art on this task in terms of rouge scores. Extensive human evaluation conducted by a radiologist further indicates that this approach yields summaries that are less likely to omit important details, without sacrificing readability or accuracy.