3260 papers • 126 benchmarks • 313 datasets
As per one of the sponsors, namely, Sen. Claro M. Recto, the supposed reading of the late national hero’s works instills an intrinsic love for the nation, the Philippines and its people. Moreover, Recto asserted that reading the masterpieces written down by Jose Rizal will further increase youth patriotism and develop their sense of Filipino identity ando add concreteness to his statement, he even fought vehemently for the deliberate study of the country’s hero’s life, works, and writings to be required of all students in all public and private schools, colleges, and institutions in order to achieve the goal. Senator Jose P. Laurel, on the other hand, also shared the same fiery passion to stress out the essence of reading the late hero’s writings as a way of resonating how patriotism in the past could also be of importance in the present day situation. He even introduced SB 438 on the 17th of April 1956, with the title Act to Make Noli Me Tangere and El Filibusterismo Compulsory Reading Matters in All Public and Private Schools, Colleges, and Universities, and for Other Purposes. They both emphasized that this is a fundamental cog and gear for all students of the country to be informed of Rizal’s patriotism and how he fought for the country through the help of his beloved quill and ink.original sentence.
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This paper presents an N-best reranking method based on keyphrase extraction that significantly improves the informativity of the generated compressions.
A novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations to take exterior semantic knowledge into account, and to design custom diversity and informativeness measures.
This work addresses the simplification problem with an encoder-decoder model coupled with a deep reinforcement learning framework, and explores the space of possible simplifications while learning to optimize a reward function that encourages outputs which are simple, fluent, and preserve the meaning of the input.
A globally normalized transition-based neural network model that achieves state-of-the-art part- of-speech tagging, dependency parsing and sentence compression results is introduced.
A fully unsupervised, extractive text summarization system that leverages a submodularity framework that allows summaries to be generated in a greedy way while preserving near-optimal performance guarantees is presented.
A way to automatically identify operations in a parallel corpus and introduce a sequence-labeling approach based on these annotations is devised, which provides insights on the types of transformations that different approaches can model.
This paper transforms the sequence to graph mapping problem to a word sequence to transition action sequence problem using a special transition system called a cache transition system, and presents a monotonic hard attention model for the transition framework to handle the strictly left-to-right alignment between each transition state and the current buffer input focus.
This work develops a more comprehensive method to generate the story AMR graph using state-of-the-art co-reference resolution and Meta Nodes and outperforms the state of the art SAS method by 1.7% F1 score in node prediction.
Although the models are underperform supervised models based on ROUGE scores, their models are competitive with a supervised baseline based on human evaluation for grammatical correctness and retention of meaning.
The use of bilingual corpora which are abundantly available for training sentence compression models are advocated and a new parallel Multilingual Compression dataset is released which can be used to evaluate compression models across languages and genres.
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