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Register a new userUnderstanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics
فهم التوظيف في تلخيص مبادرة مع فرانك: معيار لمقاييس التوظيف
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نماذج التلخيص الحديثة تولد بطلاقة للغاية ولكن في كثير من الأحيان مخرجات غير موثوق بها في كثير من الأحيان.هذه الدافع الطفرة من المقاييس التي تحاول قياس واقعية الملخصات التي تم إنشاؤها تلقائيا.نظرا لعدم وجود معايير مشتركة، لا يمكن مقارنة هذه المقاييس.علاوة على ذلك، فإن كل هذه الطرق تعالج الواقعية كمفهوم ثنائي وفشل في توفير رؤى أعمق على أنواع التناقضات التي أدلى بها أنظمة مختلفة.لمعالجة هذه القيود، نرتند نماذج من الأخطاء الواقعية واستخدامها لجمع التعليقات التوضيحية الإنسانية من الملخصات التي تم إنشاؤها من أنظمة التلخيص الحديثة عن البيانات الخاصة ب CNN / DM و XSUM.من خلال هذه التعليقات التوضيحية، نحدد نسبة الفئات المختلفة للأخطاء الواقعية ومقاييس التقويمات القياسية، والتي تبين ارتباطها بالحكم البشري بالإضافة إلى نقاط القوة والضعف المحددة.
Modern summarization models generate highly fluent but often factually unreliable outputs. This motivated a surge of metrics attempting to measure the factuality of automatically generated summaries. Due to the lack of common benchmarks, these metrics cannot be compared. Moreover, all these methods treat factuality as a binary concept and fail to provide deeper insights on the kinds of inconsistencies made by different systems. To address these limitations, we devise a typology of factual errors and use it to collect human annotations of generated summaries from state-of-the-art summarization systems for the CNN/DM and XSum datasets. Through these annotations we identify the proportion of different categories of factual errors and benchmark factuality metrics, showing their correlation with human judgement as well as their specific strengths and weaknesses.
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We study generating abstractive summaries that are faithful and factually consistent with the given articles. A novel contrastive learning formulation is presented, which leverages both reference summaries, as positive training data, and automaticall
y generated erroneous summaries, as negative training data, to train summarization systems that are better at distinguishing between them. We further design four types of strategies for creating negative samples, to resemble errors made commonly by two state-of-the-art models, BART and PEGASUS, found in our new human annotations of summary errors. Experiments on XSum and CNN/Daily Mail show that our contrastive learning framework is robust across datasets and models. It consistently produces more factual summaries than strong comparisons with post error correction, entailment-based reranking, and unlikelihood training, according to QA-based factuality evaluation. Human judges echo the observation and find that our model summaries correct more errors.
Data-to-text generation systems are trained on large datasets, such as WebNLG, Ro-toWire, E2E or DART. Beyond traditional token-overlap evaluation metrics (BLEU or METEOR), a key concern faced by recent generators is to control the factuality of the
generated text with respect to the input data specification. We report on our experience when developing an automatic factuality evaluation system for data-to-text generation that we are testing on WebNLG and E2E data. We aim to prepare gold data annotated manually to identify cases where the text communicates more information than is warranted based on the in-put data (extra) or fails to communicate data that is part of the input (missing). While analyzing reference (data, text) samples, we encountered a range of systematic uncertainties that are related to cases on implicit phenomena in text, and the nature of non-linguistic knowledge we expect to be involved when assessing factuality. We derive from our experience a set of evaluation guidelines to reach high inter-annotator agreement on such cases.
Despite significant progress in neural abstractive summarization, recent studies have shown that the current models are prone to generating summaries that are unfaithful to the original context. To address the issue, we study contrast candidate gener
ation and selection as a model-agnostic post-processing technique to correct the extrinsic hallucinations (i.e. information not present in the source text) in unfaithful summaries. We learn a discriminative correction model by generating alternative candidate summaries where named entities and quantities in the generated summary are replaced with ones with compatible semantic types from the source document. This model is then used to select the best candidate as the final output summary. Our experiments and analysis across a number of neural summarization systems show that our proposed method is effective in identifying and correcting extrinsic hallucinations. We analyze the typical hallucination phenomenon by different types of neural summarization systems, in hope to provide insights for future work on the direction.
Recent pre-trained abstractive summarization systems have started to achieve credible performance, but a major barrier to their use in practice is their propensity to output summaries that are not faithful to the input and that contain factual errors
. While a number of annotated datasets and statistical models for assessing factuality have been explored, there is no clear picture of what errors are most important to target or where current techniques are succeeding and failing. We explore both synthetic and human-labeled data sources for training models to identify factual errors in summarization, and study factuality at the word-, dependency-, and sentence-level. Our observations are threefold. First, exhibited factual errors differ significantly across datasets, and commonly-used training sets of simple synthetic errors do not reflect errors made on abstractive datasets like XSum. Second, human-labeled data with fine-grained annotations provides a more effective training signal than sentence-level annotations or synthetic data. Finally, we show that our best factuality detection model enables training of more factual XSum summarization models by allowing us to identify non-factual tokens in the training data.
Repetition in natural language generation reduces the informativeness of text and makes it less appealing. Various techniques have been proposed to alleviate it. In this work, we explore and propose techniques to reduce repetition in abstractive summ
arization. First, we explore the application of unlikelihood training and embedding matrix regularizers from previous work on language modeling to abstractive summarization. Next, we extend the coverage and temporal attention mechanisms to the token level to reduce repetition. In our experiments on the CNN/Daily Mail dataset, we observe that these techniques reduce the amount of repetition and increase the informativeness of the summaries, which we confirm via human evaluation.
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