wanyu/IteraTeR_full_sent huggingface dataset:
Original: Summarizing a document within an allocated budget while maintaining its major concepts is a challenging task . If the budget can take any arbitrary value and not known beforehand , it becomes even more difficult.
Revised: Summarizing a document within an allocated budget while maintaining its major concepts is a challenging task in natural language processing. It is even more challenging for domains where limited training data is available or scenarios in which the length of the summary is not known beforehand , it becomes even more difficult.
Edit intent: meaning-changed
Original:   We propose MLS, an end-to-end framework to generate abstractive summaries with limited training data at arbitrary compression budgets.
Revised:  At the same time, when it comes to trusting machine-generated summaries, explaining how a summary was constructed in human-understandable terms may be critical. We propose MLS, an end-to-end framework to generate abstractive summaries with limited training data at arbitrary compression budgets.
Edit intent: meaning-changed
Original:   We propose MLS, an end-to-end framework to generate abstractive summaries with limited training data at arbitrary compression budgets.
Revised:  We propose Multi-level Summarizer (MLS), a supervised method to construct abstractive summaries of a text document at controllable lengths. The key enabler of our method is an end-to-end framework to generate abstractive summaries with limited training data at arbitrary compression budgets.
Edit intent: meaning-changed
Original:   We propose MLS, an end-to-end framework to generate abstractive summaries with limited training data at arbitrary compression budgets. MLS employs a pair of supervised sequence-to-sequence networks. The first network called theMFS-Net constructs a minimal feasible summary by identifying the key concepts of the input document .
Revised:  We propose MLS, an MFS-Net constructs a minimal feasible summary by identifying the key concepts of the input document .
Edit intent: coherence
Original:  The second network called the Pointer-Magnifier then generates the final summary from the minimal feasible summary by leveraging an interpretable multi-headed attention model. Experiments on two cross-domain datasets show that MLS outperforms baseline methods over a range of success metrics including ROUGE and METEOR.
Revised:  The second network called the Pointer-Magnifier then generates the final summary from the minimal feasible summary by leveraging an interpretable multi-headed attention mechanism that computes attention distribution over an input document using an array of timestep independent semantic kernels. Each kernel optimizes a human-interpretable syntactic or semantic property. Exhaustive experiments on two low-resource datasets in the English language show that MLS outperforms baseline methods over a range of success metrics including ROUGE and METEOR.
Edit intent: meaning-changed
Original:  Experiments on two cross-domain datasets show that MLS outperforms baseline methods over a range of success metrics including ROUGE and METEOR. We observed an improvement of approximately 4\% in both metrics over the state-of-art convolutional network at lower budgets. Results from a human evaluation study also establish the effectiveness of MLS in generating complete coherent summaries at arbitrary compression budgets .
Revised:  Experiments on two cross-domain datasets show that MLS outperforms strong baselines by up to 14.70\% in the METEOR score. Human evaluation of the summaries also suggests that they capture the key concepts of the document at various length-budgets .
Edit intent: meaning-changed
Original:  This improves over the previously fastest classical (which is also the fastest quantum) algorithm due to [ADRS15] that has a time and space complexity 2^{n+o(n)}. \bullet A classical algorithm for SVP that runs in time 2^{1.73n+o(n)} time and 2^{0.5n+o(n)} space and improves over an algorithm from [CCL18] that has the same space complexity.
Revised:  This improves over the previously fastest classical (which is also the fastest quantum) algorithm due to [ADRS15] that has a time and space complexity 2^{n+o(n)}. \bullet A classical algorithm for SVP that runs in time 2^{1.73n+o(n)} time and 2^{0.5n+o(n)} space . This improves over an algorithm from [CCL18] that has the same space complexity.
Edit intent: coherence
Original:  This improves over the previously fastest classical (which is also the fastest quantum) algorithm due to [ADRS15] that has a time and space complexity 2^{n+o(n)}. \bullet A classical algorithm for SVP that runs in time 2^{1.73n+o(n)} time and 2^{0.5n+o(n)} space and improves over an algorithm from [CCL18] that has the same space complexity.
Revised:  This improves over the previously fastest classical (which is also the fastest quantum) algorithm due to [ADRS15] that has a time and space complexity 2^{n+o(n)}. \bullet A classical algorithm for SVP that runs in time 2^{1.73n+o(n)} time and 2^{0.5n+o(n)} space and improves over an algorithm of [CCL18] that has the same space complexity.
Edit intent: clarity
Original:  For any positive integer 4\leq q\leq n, our algorithm takes q^{ 11n +o(n)} time and requires poly(n)\cdot q^{16n/q^2} memory.
Revised:  For any positive integer 4\leq q\leq n, our algorithm takes q^{ 13n +o(n)} time and requires poly(n)\cdot q^{16n/q^2} memory.
Edit intent: meaning-changed
Original:  \bullet A quantum algorithm that runs in time 2^{ 0.9532 n+o(n)} and requires 2^{0.5n+o(n)} classical memory and poly(n) qubits.
Revised:  \bullet A quantum algorithm that runs in time 2^{ 0.9535 n+o(n)} and requires 2^{0.5n+o(n)} classical memory and poly(n) qubits.
Edit intent: meaning-changed
Original:  This improves over the previously fastest classical (which is also the fastest quantum) algorithm due to [ ADRS15 ] that has a time and space complexity 2^{n+o(n)}. \bullet A classical algorithm for SVP that runs in time 2^{ 1.73 n+o(n)} time and 2^{0.5n+o(n)} space.
Revised:  This improves over the previously fastest classical (which is also the fastest quantum) algorithm due to [ ADRSD15 ] that has a time and space complexity 2^{n+o(n)}. \bullet A classical algorithm for SVP that runs in time 2^{ 1.73 n+o(n)} time and 2^{0.5n+o(n)} space.
Edit intent: meaning-changed
Original:  This improves over the previously fastest classical (which is also the fastest quantum) algorithm due to [ ADRS15 ] that has a time and space complexity 2^{n+o(n)}. \bullet A classical algorithm for SVP that runs in time 2^{ 1.73 n+o(n)} time and 2^{0.5n+o(n)} space.
Revised:  This improves over the previously fastest classical (which is also the fastest quantum) algorithm due to [ ADRS15 ] that has a time and space complexity 2^{n+o(n)}. \bullet A classical algorithm for SVP that runs in time 2^{ 1.741 n+o(n)} time and 2^{0.5n+o(n)} space.
Edit intent: meaning-changed
Original: The large amount of tourism-related data presents a series of challenges for tourism demand forecasting, including data deficiencies, multicollinearity and long calculation time. A Bagging-based multivariate ensemble deep learning model, integrating Stacked Autoencoders and KELM (B-SAKE) is proposed to address these challenges in this study.
Revised: The large amount of tourism-related data presents a series of challenges for tourism demand forecasting, including data deficiencies, multicollinearity and long calculation times. A bagging-based multivariate ensemble deep learning model, integrating Stacked Autoencoders and KELM (B-SAKE) is proposed to address these challenges in this study.
Edit intent: fluency
Original:  A Bagging-based multivariate ensemble deep learning model, integrating Stacked Autoencoders and KELM (B-SAKE) is proposed to address these challenges in this study.
Revised:  A Bagging-based multivariate ensemble deep learning approach integrating stacked autoencoders and kernel-based extreme learning machines (B-SAKE) is proposed to address these challenges in this study.
Edit intent: clarity
Original: The large amount of tourism-related data presents a series of challenges for tourism demand forecasting, including data deficiencies, multicollinearity and long calculation times. A bagging-based multivariate ensemble deep learning approach integrating stacked autoencoders and kernel-based extreme learning machines (B-SAKE) is proposed to address these challenges in this study.
Revised: The large amount of tourism-related data presents a series of challenges for tourism demand forecasting, but presents significant challenges for forecasting, including curse of dimensionality and high model complexity. A novel bagging-based multivariate ensemble deep learning approach integrating stacked autoencoders and kernel-based extreme learning machines (B-SAKE) is proposed to address these challenges in this study.
Edit intent: meaning-changed
Original:  We forecast tourist arrivals in Beijing from four countries by adopting historical data on tourist arrivals in Beijing, economic indicators and online tourist behavior variables. The results from the cases of four origin countries suggest that our proposed B-SAKE approach outperforms than benchmark models in terms of horizontal accuracy, directional accuracy and  statistical significance.
Revised:  We forecast tourist arrivals in Beijing from four countries . The consistent results of multiple schemes suggest that our proposed B-SAKE approach outperforms than benchmark models in terms of horizontal accuracy, directional accuracy and statistical significance.
Edit intent: clarity
Original: In this paper we introduce a novel approach to the problem of understanding documents where  the local semantics is influenced by non-trivial layout. Namely , we modify the Transformer  architecture in a way that allows it to use the graphical features defined by the layout , without the need to re-learn the language semantics from scratch , thanks to starting the training process from a model pretrained on classical language modeling tasks .
Revised: In this paper we introduce a novel approach to the problem of understanding documents where the local semantics . To this end , we modify the Transformer architecture in a way that allows it to use the graphical features defined by the layout , without the need to re-learn the language semantics from scratch , thanks to starting the training process from a model pretrained on classical language modeling tasks .
Edit intent: clarity
Original:  Namely , we modify the Transformer  architecture in a way that allows it to use the graphical features defined by the layout , without the need to re-learn the language semantics from scratch , thanks to starting the training process from a model pretrained on classical language modeling tasks .
Revised:  Namely , we modify the Transformer encoder architecture in a way that allows it to use the graphical features defined by the layout , without the need to re-learn the language semantics from scratch , thanks to starting the training process from a model pretrained on classical language modeling tasks .
Edit intent: meaning-changed
Original:  Namely , we modify the Transformer  architecture in a way that allows it to use the graphical features defined by the layout , without the need to re-learn the language semantics from scratch , thanks to starting the training process from a model pretrained on classical language modeling tasks .
Revised:  Namely , we modify the Transformer architecture in a way that allows it to use layout features obtained from an OCR system , without the need to re-learn the language semantics from scratch , thanks to starting the training process from a model pretrained on classical language modeling tasks .
Edit intent: meaning-changed
Original:  Namely , we modify the Transformer  architecture in a way that allows it to use the graphical features defined by the layout , without the need to re-learn the language semantics from scratch , thanks to starting the training process from a model pretrained on classical language modeling tasks .
Revised:  Namely , we modify the Transformer architecture in a way that allows it to use the graphical features defined by the layout , without the need to re-learn the language semantics from scratch . We augment the input of the model only with the coordinates of token bounding boxes, avoiding the use of raw images. This leads to a layout-aware language model which can be then fine-tuned on downstream tasks. The model is evaluated on an end-to-end information extraction task using four publicly available datasets: Kleister NDA, Kleister Charity, SROIE and CORD. We show that it achieves superior performance on datasets consisting of visually rich documents, at the same time outperforming the baseline RoBERTa on documents with flat layout (NDA F1 increase from 78.50 to 80.42). Our solution ranked 1st on the public leaderboard for the Key Information Extraction from the SROIE dataset, improving the SOTA F1-score from 97.81 to 98.17 .
Edit intent: meaning-changed
Original:  We show that it achieves superior performance on datasets consisting of visually rich documents, at the same time outperforming the baseline RoBERTa on documents with flat layout (NDA F1\) } increase from 78.50 to 80.42).
Revised:  We show that our model achieves superior performance on datasets consisting of visually rich documents, at the same time outperforming the baseline RoBERTa on documents with flat layout (NDA F1\) } increase from 78.50 to 80.42).
Edit intent: clarity
Original:  We show that it achieves superior performance on datasets consisting of visually rich documents, at the same time outperforming the baseline RoBERTa on documents with flat layout (NDA F1\) } increase from 78.50 to 80.42).
Revised:  We show that it achieves superior performance on datasets consisting of visually rich documents, while also outperforming the baseline RoBERTa on documents with flat layout (NDA F1\) } increase from 78.50 to 80.42).
Edit intent: clarity
Original:  We show that it achieves superior performance on datasets consisting of visually rich documents, at the same time outperforming the baseline RoBERTa on documents with flat layout (NDA F1\) } increase from 78.50 to 80.42).
Revised:  We show that it achieves superior performance on datasets consisting of visually rich documents, at the same time outperforming the baseline RoBERTa on documents with flat layout (NDA \(F_{1\) } increase from 78.50 to 80.42).
Edit intent: clarity
Original:  Our solution ranked 1st on the public leaderboard for the Key Information Extraction from the SROIE dataset, improving the SOTA F1-score\)-score } from 97.81 to 98.17.
Revised:  Our solution ranked first on the public leaderboard for the Key Information Extraction from the SROIE dataset, improving the SOTA F1-score\)-score } from 97.81 to 98.17.
Edit intent: fluency
Original:  Our solution ranked 1st on the public leaderboard for the Key Information Extraction from the SROIE dataset, improving the SOTA F1-score\)-score } from 97.81 to 98.17.
Revised:  Our solution ranked 1st on the public leaderboard for the Key Information Extraction from the SROIE dataset, improving the SOTA \(F_{1\)-score } from 97.81 to 98.17.
Edit intent: clarity
Original:  Apart from the classical random graph families including random, scale-free and small world graphs, we introduced a novel and flexible algorithm for directly generating random directed acyclic graphs (DAG) and studied a class of graphs derived from functional resting state fMRI networks. A majority of the best performing networks were indeed in these new families. We also proposed a general procedure for turning a graph into a DAG necessary for a feed-forward neural network. We investigated various structural and numerical properties of the graphs in relation to neural network test accuracy.
Revised:  We investigate various structural and numerical properties of the graphs in relation to neural network test accuracy.
Edit intent: coherence
Original:  Since none of the classical numerical graph invariants by itself seems to allow to single out the best networks , we introduced new numerical characteristics that selected a set of quasi-1-dimensional graphs, which were the majority among the best performing networks. 
Revised:  We find that none of the classical numerical graph invariants by itself seems to allow to single out the best networks , we introduced new numerical characteristics that selected a set of quasi-1-dimensional graphs, which were the majority among the best performing networks. 
Edit intent: coherence
Original:  Since none of the classical numerical graph invariants by itself seems to allow to single out the best networks , we introduced new numerical characteristics that selected a set of quasi-1-dimensional graphs, which were the majority among the best performing networks. 
Revised:  Since none of the classical numerical graph invariants by itself allows to single out the best networks , we introduced new numerical characteristics that selected a set of quasi-1-dimensional graphs, which were the majority among the best performing networks. 
Edit intent: coherence
Original:  Since none of the classical numerical graph invariants by itself seems to allow to single out the best networks , we introduced new numerical characteristics that selected a set of quasi-1-dimensional graphs, which were the majority among the best performing networks. 
Revised:  Since none of the classical numerical graph invariants by itself seems to allow to single out the best networks . Consequently, we introduce a new numerical graph characteristic that selects a set of quasi-1-dimensional graphs, which were the majority among the best performing networks. 
Edit intent: coherence
Original:  Since none of the classical numerical graph invariants by itself seems to allow to single out the best networks , we introduced new numerical characteristics that selected a set of quasi-1-dimensional graphs, which were the majority among the best performing networks. 
Revised:  Since none of the classical numerical graph invariants by itself seems to allow to single out the best networks , we introduced new numerical characteristics that selected a set of quasi-1-dimensional graphs, which are a majority among the best performing networks. 
Edit intent: fluency
Original:  Since none of the classical numerical graph invariants by itself seems to allow to single out the best networks , we introduced new numerical characteristics that selected a set of quasi-1-dimensional graphs, which were the majority among the best performing networks. 
Revised:  Since none of the classical numerical graph invariants by itself seems to allow to single out the best networks , we introduced new numerical characteristics that selected a set of quasi-1-dimensional graphs, which were the majority among the best performing networks. We also find that networks with primarily short-range connections perform better than networks which allow for many long-range connections. Moreover, many resolution reducing pathways are beneficial. We provide a dataset of 1020 graphs and the test accuracies of their corresponding neural networks at URL
Edit intent: meaning-changed
Original: For any matching market where the set of stable matchings has a lattice structure, if the binary operations ( l.u.b. and g.l.b. ) are computed via a "pointing function", then the set of random matchings (lotteries of stable matchings) has a lattice structure .
Revised: In this paper we study the lattice structure of the random matching set, that is, all lotteries of stable matchings has a lattice structure, if the binary operations ( l.u.b. and g.l.b. ) are computed via a "pointing function", then the set of random matchings (lotteries of stable matchings) has a lattice structure .
Edit intent: meaning-changed
Original: For any matching market where the set of stable matchings has a lattice structure, if the binary operations ( l.u.b. and g.l.b. ) are computed via a "pointing function", then the set of random matchings (lotteries of stable matchings) has a lattice structure .
Revised: For any matching market where the set of stable matchings . We define the l.u.b. and g.l.b. ) are computed via a "pointing function", then the set of random matchings (lotteries of stable matchings) has a lattice structure .
Edit intent: meaning-changed
Original: For any matching market where the set of stable matchings has a lattice structure, if the binary operations ( l.u.b. and g.l.b. ) are computed via a "pointing function", then the set of random matchings (lotteries of stable matchings) has a lattice structure .
Revised: For any matching market where the set of stable matchings has a lattice structure, if the binary operations ( l.u.b. (least upper bound) and the g.l.b. ) are computed via a "pointing function", then the set of random matchings (lotteries of stable matchings) has a lattice structure .
Edit intent: meaning-changed
Original: For any matching market where the set of stable matchings has a lattice structure, if the binary operations ( l.u.b. and g.l.b. ) are computed via a "pointing function", then the set of random matchings (lotteries of stable matchings) has a lattice structure .
Revised: For any matching market where the set of stable matchings has a lattice structure, if the binary operations ( l.u.b. and g.l.b. (greatest lower bound) for both sides of the matching market, and we prove the that with these binary operations the set of random matchings (lotteries of stable matchings) has a lattice structure .
Edit intent: meaning-changed
Original: For any matching market where the set of stable matchings has a lattice structure, if the binary operations ( l.u.b. and g.l.b. ) are computed via a "pointing function", then the set of random matchings (lotteries of stable matchings) has a lattice structure .
Revised: For any matching market where the set of stable matchings has a lattice structure, if the binary operations ( l.u.b. and g.l.b. ) are computed via a "pointing function", then the set of random matchings has two dual lattices .
Edit intent: meaning-changed
Original:  We define the l.u.b. ( least upper bound ) and theg.l.b. ( greatest lower bound ) for both sides of the matching market, and we prove the that with these binary operations the set of random matchings has two dual lattices.
Revised:  We define the ( least upper bound ) and theg.l.b. ( greatest lower bound ) for both sides of the matching market, and we prove the that with these binary operations the set of random matchings has two dual lattices.
Edit intent: clarity
Original:  We define the l.u.b. ( least upper bound ) and theg.l.b. ( greatest lower bound ) for both sides of the matching market, and we prove the that with these binary operations the set of random matchings has two dual lattices.
Revised:  We define the l.u.b. least upper bound ) and theg.l.b. ( greatest lower bound ) for both sides of the matching market, and we prove the that with these binary operations the set of random matchings has two dual lattices.
Edit intent: fluency
Original:  We define the l.u.b. ( least upper bound ) and theg.l.b. ( greatest lower bound ) for both sides of the matching market, and we prove the that with these binary operations the set of random matchings has two dual lattices.
Revised:  We define the l.u.b. ( least upper bound g.l.b. ( greatest lower bound ) for both sides of the matching market, and we prove the that with these binary operations the set of random matchings has two dual lattices.
Edit intent: coherence
Original:  We define the l.u.b. ( least upper bound ) and theg.l.b. ( greatest lower bound ) for both sides of the matching market, and we prove the that with these binary operations the set of random matchings has two dual lattices.
Revised:  We define the l.u.b. ( least upper bound ) and the ( greatest lower bound ) for both sides of the matching market, and we prove the that with these binary operations the set of random matchings has two dual lattices.
Edit intent: clarity