What Makes a Good Summary? Reconsidering the Focus of Automatic Summarization

• URL: http://arxiv.org/abs/2012.07619v1
• Date: Mon, 14 Dec 2020 15:12:35 GMT
• Title: What Makes a Good Summary? Reconsidering the Focus of Automatic Summarization
• Authors: Maartje ter Hoeve, Julia Kiseleva, Maarten de Rijke
• Abstract summary: We find that the current focus of the field does not fully align with participants' wishes. Based on our findings, we argue that it is important to adopt a broader perspective on automatic summarization.
• Score: 49.600619575148706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic text summarization has enjoyed great progress over the last years. Now is the time to re-assess its focus and objectives. Does the current focus fully adhere to users' desires or should we expand or change our focus? We investigate this question empirically by conducting a survey amongst heavy users of pre-made summaries. We find that the current focus of the field does not fully align with participants' wishes. In response, we identify three groups of implications. First, we argue that it is important to adopt a broader perspective on automatic summarization. Based on our findings, we illustrate how we can expand our view when it comes to the types of input material that is to be summarized, the purpose of the summaries and their potential formats. Second, we define requirements for datasets that can facilitate these research directions. Third, usefulness is an important aspect of summarization that should be included in our evaluation methodology; we propose a methodology to evaluate the usefulness of a summary. With this work we unlock important research directions for future work on automatic summarization and we hope to initiate the development of methods in these directions.

Related papers

• Incremental Extractive Opinion Summarization Using Cover Trees [81.59625423421355]
  In online marketplaces user reviews accumulate over time, and opinion summaries need to be updated periodically. In this work, we study the task of extractive opinion summarization in an incremental setting. We present an efficient algorithm for accurately computing the CentroidRank summaries in an incremental setting.
  arXiv  Detail & Related papers  (2024-01-16T02:00:17Z)
• Summarization with Graphical Elements [55.5913491389047]
  We propose a new task: summarization with graphical elements. We collect a high quality human labeled dataset to support research into the task.
  arXiv  Detail & Related papers  (2022-04-15T17:16:41Z)
• Automatic Text Summarization Methods: A Comprehensive Review [1.6114012813668934]
  This study provides a detailed analysis of text summarization concepts such as summarization approaches, techniques used, standard datasets, evaluation metrics and future scopes for research.
  arXiv  Detail & Related papers  (2022-03-03T10:45:00Z)
• AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization [73.91543616777064]
  Community Question Answering (CQA) fora such as Stack Overflow and Yahoo! Answers contain a rich resource of answers to a wide range of community-based questions. One goal of answer summarization is to produce a summary that reflects the range of answer perspectives. This work introduces a novel dataset of 4,631 CQA threads for answer summarization, curated by professional linguists.
  arXiv  Detail & Related papers  (2021-11-11T21:48:02Z)
• Question-aware Transformer Models for Consumer Health Question Summarization [20.342580435464072]
  We develop an abstractive question summarization model that leverages the semantic interpretation of a question via recognition of medical entities. When evaluated on the MeQSum benchmark corpus, our framework outperformed the state-of-the-art method by 10.2 ROUGE-L points.
  arXiv  Detail & Related papers  (2021-06-01T04:21:31Z)
• Extracting actionable information from microtexts [0.0]
  This dissertation proposes a semi-automatic method for extracting actionable information. We show that predicting time to event is possible for both in-domain and cross-domain scenarios. We propose a method to integrate the machine learning based relevant information classification method with a rule-based information classification technique to classify microtexts.
  arXiv  Detail & Related papers  (2020-08-01T21:22:53Z)
• From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information [77.89755281215079]
  Text summarization is the research area aiming at creating a short and condensed version of the original document. In real-world applications, most of the data is not in a plain text format. This paper focuses on the survey of these new summarization tasks and approaches in the real-world application.
  arXiv  Detail & Related papers  (2020-05-10T14:59:36Z)
• Few-Shot Learning for Opinion Summarization [117.70510762845338]
  Opinion summarization is the automatic creation of text reflecting subjective information expressed in multiple documents. In this work, we show that even a handful of summaries is sufficient to bootstrap generation of the summary text. Our approach substantially outperforms previous extractive and abstractive methods in automatic and human evaluation.
  arXiv  Detail & Related papers  (2020-04-30T15:37:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.