Task Proposal: Abstractive Snippet Generation for Web Pages (2020)

Abstract: We propose a shared task on abstractive snippet generation for web pages, a novel task of generating query-biased abstractive summaries for documents that are to be shown on a search results page. Conventional snippets are extractive in nature, which recently gave rise to copyright claims from news publishers as well as a new copyright legislation being passed in the European Union, limiting the fair use of web page contents for snippets. At the same time, abstractive summarization has matured considerably in recent years, potentially allowing for more personalization of snippets in the future. Taken together, these facts render further research into generating abstractive snippets both timely and promising.

## motivation a snippet on a search results page is a short summary accompanying each retrieved web page for a given user query. conventionally, snippets are extractive summaries, where a few sentences containing the query's terms are extracted from the web page and arranged for display. with the query's terms highlighted in bold, one may decide at a glance whether a given web page merits further investigation with respect to one's information need. however, despite the relative ease with which snippets can be generated, and their near-universal use on modern web search engines, extractive snippets have limitations in terms of their expressiveness. more urgently, they have also increasingly become subject to copyright disputes and claims, foot_0 in particular from news publishers who lobbied for fair use, rendering extractive snippets from news almost or even entirely infeasible in some countries. as a way forward, we envision abstractive snippets as an alternative @xcite . an abstractive snippet is a query-biased abstractive summary of a web page that has minimal text reuse. abstractive snippets have been shown to be on a par with extractive ones in terms of enabling search engine users to identify relevant results @xcite . further, they are ideally suited to advance explainability and personalization of search results @xcite . explanations may include reasons for ranking a page high or low, and personalization may hint either at information that the user has not seen elsewhere, or at adaptations to the user's experience level on a given subject. owing to the recent advances in abstractive summarization @xcite ) and text synthesis technology @xcite , we believe this is the right time to delve into abstractive query: treasury of humor snippet: anchor context asimov, on the other hand, proposes (in his first jokebook, treasury of humor) that the essence of humour is anticlimax: an abrupt change in point of view, in which trivial matters are suddenly elevated in importance above those that would normally be far more important. document [ . . . ] treasury of humor is unique in that in addition to being a working joke book, it is a treatise on the theory of humor, propounding asimov's theory that the essence of humor is an abrupt, jarring change in emphasis and/or point of view, moving from the crucial to the trivial, and/or from the sublime to the ridiculous [ . . . ] given the many relevant text generation tasks hosted at inlg, our task makes for a strong addition to this catalog, inviting participants not only from the nlp community but also from information retrieval. building on a long and successful series of shared tasks in various domains, 3 including the recent tl;dr summarization challenge at inlg @xcite , we strive to provide a supportive infrastructure, rigorous evaluations, and to share useful insights with the research community. ## task description participants may employ any abstractive summarization technology, with the aim of generating summaries (in the sense of snippets) that are querybiased. among the first to study this task was @xcite , who summarized news documents by using named entities as queries from the cnn/dailymail dataset @xcite . recently, @xcite proposed a model employing bidirectional generation to induce the query bias, xie et al. (2020) use conditional self-attention, roitman et al. (2020) unsupervised learning, and mao et al. (2020) lexically constrained decoding. 3 https://webis.de/events.html?@xmath0: customer respect index ## conclusion we believe that our shared task will open new avenues to study abstractive snippet generation and query-biased summarization in general. by analyzing the performance of existing abstractive summarization technology from various perspectives, carrying out a comprehensive qualitative evaluation, and openly publishing all our data, code, and findings, we intend to make a meaningful contribution to the community in constrained text generation. abstractive summarization may hold the key to future web search technology, where a search engine not only explains to its users how a given web page is relevant to their current information need, but also why a given web page might be particularly relevant to them, personally. although, even with current technology, we are still far removed from pursuing this goal, enabling contrained abstractive summarization is the first into this direction. moreover, since web search engines currently operate perhaps the largest deployments of summarization technology, it is vitally important for our information society's ecosystem to maintain the ability to generate snippets in a copyright-compliant way.