We investigate how abstractive methods can be applied to QFS, to overcome such limitations. Such methods, however, produce text which suffers from low coherence. Query Focused Summarization (QFS) has been addressed mostly using extractive methods. We discuss the potential usage of such Q2ID technique through an example application. We demonstrate the effectiveness of our model by comparing with several state-of-the-art generation models on the Q2ID task. To address this new task, we propose a novel Contrastive Generation model, namely CtrsGen for short, to generate the intent description by contrasting the relevant documents with the irrelevant documents given a query. Unlike those existing ranking tasks which leverage the query and its description to compute the relevance of documents, Q2ID is a reverse task which aims to generate a natural language intent description based on both relevant and irrelevant documents of a given query. In this paper, therefore, we propose a novel Query-to-Intent-Description (Q2ID) task for query understanding. If a system could automatically generate a detailed and precise intent description for a search query, like human annotators, that would indicate much better query understanding has been achieved. As we may find in many benchmark datasets, e.g., TREC and SemEval, queries are often associated with a detailed description provided by human annotators which clearly describes its intent to help evaluate the relevance of the documents. ![]() However, it is not that precise to understand a search query at the intent class/cluster level due to the loss of many detailed information. Many different tasks have been proposed for understanding users' search queries, e.g., query classification or query clustering. ![]() Join us on Twitter, Facebook, LinkedIn, Instagram, Pinterest, Tumblr, and YouTube.Query understanding is a fundamental problem in information retrieval (IR), which has attracted continuous attention through the past decades. Sign up to receive product news, free updates, specials, and support for Markzware products. Markzware supports graphic software layout applications for printers, publishers, and graphic arts professionals. Markzware, a privately-held company based in Dayton, Nevada, is the leading software publisher of solutions for document preview, data conversion and print quality control. (A Windows version is in the works.) You can purchase a perpetual license of QXPMarkz, via the QXPMarkz page on and through authorized Resellers. System requirements include macOS 10.12 or newer, 4GB of RAM, 1024×768 display, and Internet connection. Video: Preview and Convert QuarkXPress to InDesign and more You can even view a file inspector panel with details, including number of images and fonts detected in the file. You get a rough preview of the QXP file and can export this preview as several formats, including PNG and JPEG. QXPMarkz can export QXP file text and save it as plain text, RTF or HTML. Send IDML files to Affinity Publisher version 1.8+. ![]() Convert Quark files to InDesign, without having QuarkXPress on your computer. Convert QuarkXPress to InDesign 2021 on macOS!Īmybeth Menendez, Assistant Manager of Print Workflow, Macmillian Publishers, said, "As an avid InDesign user, … my first impression was … 'Is going to replace Q2ID?!' … it should! This product has a stand-alone drag and drop UI, that … is pretty awesome! … the new UI … details the version of Quark it was created in, the fonts, colors, page sizes, page count, images used and/or missing, etc.! All in an easy to interpret interface! Now, you can export to InDesign, Publisher, Acrobat, and even Illustrator! … Even users without the target software can open and basic preview and preflight and export the files to destination!"
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