DataHammer/scimrc huggingface dataset:
Question: How to test the modifications for multi-document summarization?
Evidence: Extractive multi-document summarization (MDS) aims to summarize a collection of documents by selecting a small number of sentences that represent the original content appropriately. Typical objectives for assembling a summary include information coverage and non-redundancy. A wide variety of methods have been introduced to approach MDS.
Many approaches are based on sentence ranking, i.e. assigning each sentence a score that indicates how well the sentence summarizes the input BIBREF0 , BIBREF1 , BIBREF2 . A summary is created by selecting the top entries of the ranked list of sentences. Since the sentences are often treated separately, these models might allow redundancy in the summary. Therefore, they are often extended by an anti-redundancy filter while de-queuing ranked sentence lists.
Other approaches work at summary-level rather than sentence-level and aim to optimize functions of sets of sentences to find good summaries, such as KL-divergence between probability distributions BIBREF3 or submodular functions that represent coverage, diversity, etc. BIBREF4 
The centroid-based model belongs to the former group: it represents sentences as bag-of-word (BOW) vectors with TF-IDF weighting and uses a centroid of these vectors to represent the whole document collection BIBREF5 . The sentences are ranked by their cosine similarity to the centroid vector. This method is often found as a baseline in evaluations where it usually is outperformed BIBREF0 , BIBREF6 .
This baseline can easily be adapted to work at the summary-level instead the sentence level. This is done by representing a summary as the centroid of its sentence vectors and maximizing the similarity between the summary centroid and the centroid of the document collection. A simple greedy algorithm is used to find the best summary under a length constraint.
In order to keep the method efficient, we outline different methods to select a small number of candidate sentences from each document in the input collection before constructing the summary.
We test these modifications on the DUC2004 dataset for multi-document summarization. The results show an improvement of Rouge scores over the original centroid method. The performance is on par with state-of-the-art methods which shows that the similarity between a summary centroid and the input centroid is a well-suited function for global summary optimization.
The summarization approach presented in this paper is fast, unsupervised and simple to implement. Nevertheless, it performs as well as more complex state-of-the-art approaches in terms of Rouge scores on the DUC2004 dataset. It can be used as a strong baseline for future research or as a fast and easy-to-deploy summarization tool.
We test these modifications on the DUC2004 dataset for multi-document summarization.
Answer: The authors test these modifications on the DUC2004 dataset for multi-document summarization.
Question: Do the authors report results only on English datasets?
Evidence: Our study focused primarily on English tweets, since this was the language of our diagnostic training sample. Future studies could incorporate other languages using our proposed framework. It would be important to also expand the API queries with translations of `breast' and `cancer'. This could allow for a cross cultural comparison of how social media influences patients and what patients express on social media.
Our study focused primarily on English tweets, since this was the language of our diagnostic training sample.
Answer: Yes.
Question: Write me a thank you note for my high-school teacher
Answer: Thank you for being a supportive teacher and encouraging me to keep learning. I will always remember your encouragement as I move forward in my studies.
Question: What are the names of popular Alternative music bands from the 1980s and 1990s.
Answer: Bands that were considered Alternative rock music in the 1980s were R.E.M., Depeche Mode, Nirvana, O.M.D., the Smiths, the Psychadelic Furs, the Housemartins, Green Day, the Violent Femmes, the B-52s, the Cure, UB40, and U2, just to name a few. Keep in mind that some of the bands that had been considered Alternative in the 1980s became more mainstream in the 1990s as they gained popularity (R.E.M., Depeche Mode, Green Day, U2, The Cure). The 1990s Alternative rock music bands that I remember are the Counting Crows, the Red Hot Chili Peppers, Third Eye Blind, the Stone Temple Pilots, Live, Toad the Wet Sprocket, Blues Traveler, the Spin Doctors, and No Doubt. In the 2000's, the popular Alternative rock music bands were Maroon 5, Coldplay, Train, the White Stripes, Linkin Park, Gorillaz, Kings of Leon, Weezer, MGMT, Nickelback, Radiohead, Goo Goo Dolls, Blink-182.
Question: Earnst & Young Entrepreneur of the year awards 2021 was given to whom
Answer: Falguni Nayar(founder and CEO of Nykaa)
Question: What colors are on the United States of America flag?
Answer: Red, white, blue
Question: What is the biggest fish in the ocean?
Answer: Whale Sharks are the biggest fish in the ocean coming in at 23 feet. Several species of whales are much larger, but they are technically mammals.
Question: Given this paragraph about Thanjavur paintings and its origins, what are the typical features of Thanjavur paintings?
Evidence: Thanjavur painting is a classical South Indian painting style, which was inaugurated from the town of Thanjavur (anglicized as Tanjore) in Tamil Nadu. The art form draws its immediate resources and inspiration from way back about 1600 AD, a period when the Nayakas of Thanjavur under the suzerainty of the Vijayanagara Rayas encouraged art—chiefly, classical dance and music—as well as literature, both in Telugu and Tamil and painting of chiefly Hindu religious subjects in temples. It is distinguished by its famous gold coating. However, it can safely be surmised that Thanjavur painting, as we know it now, originated in the Maratha court of Thanjavur (1676–1855).It has been recognized as a Geographical indication by the Government of India in 2007–08. Thanjavur paintings are characterized by rich and vivid colors, simple icon composition, glittering gold foils overlaid on delicate but extensive gesso work and inlay of glass beads and pieces or precious and semi-precious gems. In Thanjavur paintings one can see the influence of Deccani, Vijayanagar, Maratha and even European or Company styles of painting. Essentially serving as devotional icons, the subjects of most paintings are Hindu gods, goddesses, and saints. Episodes from Hindu Puranas, Sthala-puranas and other religious texts were visualized, sketched or traced and painted with the main figure or figures placed in the central section of the picture (mostly within an architecturally delineated space such as a mantapa or prabhavali) surrounded by several subsidiary figures, themes and subjects. There are also many instances when Jain, Sikh, Muslim, other religious and even secular subjects were depicted in Tanjore paintings. Thanjavur paintings are panel paintings done on wooden planks, and hence referred to as Palagai padam in local parlance. In modern times, these paintings have become souvenirs for festive occasions in South India.
Answer: Thanjavur paintings are easily discernible from the gold coating on wooden panels which form the base. They are characterized by  vivid colors, simple composition of icons along with usage of glass beads and at times precious stones. The subjects of paintings have traditionally been Hindus gods though they have evolved to depict secular subjects from other religions.
Question: How do I cook basmati rice?
Answer: To cook basmati rice perfectly, first rinse the desired amount of rice until the water runs clear. Then, in a saucepan with a tight fitting lid and over medium heat, add rice and water in a 1C rice to 1.5C water or other liquid (like broth) ratio. Bring the pot to a simmer, reduce heat to low, cover and let cook for 15 minutes. Let the rice sit for another 10 minutes off of the heat with the lid on and the fluff with a fork and enjoy! To make the rice more flavorful, you can add cumin or other spices and salt.
Question: Wat id DNA annotation?
Answer: DNA annotation or genome annotation is the process of identifying the locations of genes and all of the coding regions in a genome and determining what those genes do. An annotation (irrespective of the context) is a note added by way of explanation or commentary. Once a genome is sequenced, it needs to be annotated to make sense of it. Genes in a eukaryotic genome can be annotated using various annotation tools such as FINDER. A modern annotation pipeline can support a user-friendly web interface and software containerization such as MOSGA. Modern annotation pipelines for prokaryotic genomes are Bakta, Prokka and PGAP.

For DNA annotation, a previously unknown sequence representation of genetic material is enriched with information relating genomic position to intron-exon boundaries, regulatory sequences, repeats, gene names and protein products. This annotation is stored in genomic databases such as Mouse Genome Informatics, FlyBase, and WormBase. Educational materials on some aspects of biological annotation from the 2006 Gene Ontology annotation camp and similar events are available at the Gene Ontology website.

The National Center for Biomedical Ontology develops tools for automated annotation of database records based on the textual descriptions of those records.

As a general method, dcGO has an automated procedure for statistically inferring associations between ontology terms and protein domains or combinations of domains from the existing gene/protein-level annotations.
Question: Were cars used for transportation in the 19th century in King Field?
Evidence: King Field (alternately, Kingfield) is a neighborhood in the Southwest community in Minneapolis, Minnesota. Its boundaries are 36th Street to the north, Interstate 35W to the east, 46th Street to the south, and Lyndale Avenue to the west. King Field, within the King Field neighborhood is a park named after Martin Luther King Jr.

This was farm country in the 2nd half of the 19th century. Transportation was by horse and buggy. Fewer than 20 farms had been established by 1874. C.C. Garvey owned a dairy farm near 44th Street and Grand Ave. George Bichnell farmed 18 acres (73,000 m2) of land from Lyndale to Pleasant, 42nd to 43rd Streets. Hiram Van Nest farmed 28 acres (110,000 m2) of land from 40th to 42nd, Pleasant to Lyndale. The Farmsworth farm occupied 57 acres (230,000 m2) south of 47th Street and East of Nicollet.
Answer: No, transportation was by horse and buggy
Question: What are the evaluation metrics used?
Evidence: When testing across languages, we report accuracy for two setups: average accuracy over each single-language model (Avg), and accuracy obtained when training on the concatenation of all languages but the target one (All). The latter setting is also used for the embeddings model. We report accuracy for all experiments.
When testing across languages, we report accuracy for two setups: average accuracy over each single-language model (Avg), and accuracy obtained when training on the concatenation of all languages but the target one (All). The latter setting is also used for the embeddings model. We report accuracy for all experiments.
Answer: The answers are shown as follows:
* average accuracy over each single-language model (Avg), and accuracy obtained when training on the concatenation of all languages but the target one (All)
Question: What is the greenhouse effect?
Answer: The greenhouse effect is the process by which the sun's energy gets trapped in a planet's atmosphere and heat's the planet's surface. The energy gets trapped due to the planet's atmosphere having a higher transparency to visible radiation from the sun than it does to infrared radiation that gets emitted after bouncing off of the planet's surface.
Question: What is the oldest part of Boston to live in, based on the historic neighborhood?
Evidence: Charlestown is the oldest neighborhood in Boston, Massachusetts, in the United States. Originally called Mishawum by the Massachusett tribe, it is located on a peninsula north of the Charles River, across from downtown Boston, and also adjoins the Mystic River and Boston Harbor waterways. Charlestown was laid out in 1629 by engineer Thomas Graves, one of its earliest settlers, during the reign of Charles I of England. It was originally a separate town and the first capital of the Massachusetts Bay Colony.

Charlestown became a city in 1848 and was annexed by Boston on January 5, 1874. With that, it also switched from Middlesex County, to which it had belonged since 1643, to Suffolk County. It has had a substantial Irish-American population since the migration of Irish people during the Great Irish Famine of the 1840s. Since the late 1980s, the neighborhood has changed dramatically because of its proximity to downtown and its colonial architecture. A mix of yuppie and upper-middle-class gentrification has influenced much of the area, as it has in many of Boston's neighborhoods, but Charlestown still maintains a strong Irish-American population.

In the 21st century, Charlestown's diversity has expanded dramatically, along with growing rates of the very poor and very wealthy. Today Charlestown is a largely residential neighborhood, with much housing near the waterfront, overlooking the Boston skyline. Charlestown is home to many historic sites, hospitals and organizations, with access from the Orange Line Sullivan Square or Community College stops or the I-93 expressway.
Answer: Charlestown, Massachusetts
Question: The LIVA Miss Diva Supranational 2021 was given to whom?
Answer: Ritika Khatnani (Pune)
Question: what word embedding techniques did they experiment with?
Evidence: In this work, we use the Python Word2Vec implementation provided by the Gensim library. The Portuguese pre-trained word embeddings created by BIBREF10 and available for download was used to represent words as vectors. We only used models of dimension 300 and, for Word2Vec, Wang2Vec, and FastText, skip-gram architectured models.
We only used models of dimension 300 and, for Word2Vec, Wang2Vec, and FastText, skip-gram architectured models.
Answer: The answers are shown as follows:
* Word2Vec, Wang2Vec, and FastText
Question: Based on the passage provided, what are the only two elements in the periodic table that were names after a person who was alive at the time of naming?
Evidence: Oganesson is a synthetic chemical element with the symbol Og and atomic number 118. It was first synthesized in 2002 at the Joint Institute for Nuclear Research (JINR) in Dubna, near Moscow, Russia, by a joint team of Russian and American scientists. In December 2015, it was recognized as one of four new elements by the Joint Working Party of the international scientific bodies IUPAC and IUPAP. It was formally named on 28 November 2016. The name honors the nuclear physicist Yuri Oganessian, who played a leading role in the discovery of the heaviest elements in the periodic table. It is one of only two elements named after a person who was alive at the time of naming, the other being seaborgium, and the only element whose eponym is alive as of 2023.
Answer: Organesson and Seaborgium are the only two elements in the periodic table that were names after a person who was alive at the time of naming.
Question: Give me a bulleted list of the 7 wonders of the ancient world:
Answer: Here are the 7 wonders of the ancient world:

- Great Pyramid of Giza, in El Giza, Egypt, the earliest of the wonders to be completed, as well as the only one that still exists in the present day.
- Colossus of Rhodes, in the harbor of the city of Rhodes, on the Greek island of the same name.
- Hanging Gardens of Babylon, in Babylon, near present-day Hillah, Babil province, Iraq; or Nineveh, Mosul, Nineveh Governorate, Iraq.
- Lighthouse of Alexandria, in Alexandria, Egypt.
- Mausoleum at Halicarnassus, in Halicarnassus, a city of the Achaemenid Empire in present-day Turkey.
- Statue of Zeus at Olympia, in Olympia, Greece.
- Temple of Artemis at Ephesus, in the city of Ephesus, near present-day Selçuk, Turkey.
Question: Which of these colors are primary colors?
Answer: Blue, Green, Violet, Orange, Indigo, Turquoise, Red
Question: Imagine you are the last person on Earth. Write a diary entry describing your thoughts and feelings.
Answer: I finally witness the day when humanity was brought to an end. It wasn't due to a technological explosion, depletion of resources, or uncontrolled wars. It was arrogance, the innate arrogance of humanity, that brought everything to an end. And now, this is the end of my life but will also mark the beginning of all cycles.
Question: Which three neural machine translation systems are analyzed?
Evidence: We train three systems (S1, S2 and S3) with the corpora summarised in Table TABREF5. The first two systems are transformer models trained on different amounts of data (6M vs. 18M parallel sentences as seen in the Table). The third system includes a modification to consider the information of full coreference chains throughout a document augmenting the sentence to be translated with this information and it is trained with the same amount of sentence pairs as S1. A variant of the S3 system participated in the news machine translation of the shared task held at WMT 2019 BIBREF43.
We train three systems (S1, S2 and S3) with the corpora summarised in Table TABREF5. The first two systems are transformer models trained on different amounts of data (6M vs. 18M parallel sentences as seen in the Table). The third system includes a modification to consider the information of full coreference chains throughout a document augmenting the sentence to be translated with this information and it is trained with the same amount of sentence pairs as S1.
Answer: The answers are shown as follows:
* first two systems are transformer models trained on different amounts of data
* The third system includes a modification to consider the information of full coreference chains
Question: How is the delta-softmax calculated?
Evidence: We reason that, if a token is important for predicting the correct label, masking it will degrade the model's classification accuracy, or at least reduce its reported classification certainty. In SECREF36, it seems reasonable to assume that masking the word `To' has a greater impact on predicting the label purpose than masking the word `provide', and even less so, the following noun `information'. We therefore use reduction in softmax probability of the correct relation as our signaling strength metric for the model. We call this metric ${\Delta }_s$ (for delta-softmax), which can be written as:
where $rel$ is the true relation of the EDU pair, $t_i$ represents the token at index $i$ of $N$ tokens, and $X_{mask=i}$ represents the input sequence with the masked position $i$ (for $i \in 1 \ldots N$ ignoring separators, or $\phi $, the empty set).
We therefore use reduction in softmax probability of the correct relation as our signaling strength metric for the model. We call this metric ${\Delta }_s$ (for delta-softmax), which can be written as:

where $rel$ is the true relation of the EDU pair, $t_i$ represents the token at index $i$ of $N$ tokens, and $X_{mask=i}$ represents the input sequence with the masked position $i$ (for $i \in 1 \ldots N$ ignoring separators, or $\phi $, the empty set).
Answer: Answer with content missing: (Formula) Formula is the answer.
Question: What are equity securities?
Answer: Equity securities are financial assets that represent ownership of a corporation. The most prevalent type of equity security is common stock. And the characteristic that most defines an equity security—differentiating it from most other types of securities—is ownership.
Question: What toolkit is used to extract baseline features?
Evidence: After we obtain the representation of the source-target word pair by the convolution layer, we follow a similar architecture as BIBREF6 to refine the representation of the word pairs using feed-forward and recurrent networks.
Two feed-forward layers of size 400 with rectified linear units (ReLU; BIBREF9 );
One bi-directional gated recurrent unit (BiGRU; BIBREF10 ) layer with hidden size 200, where the forward and backward hidden states are concatenated and further normalized by layer normalization BIBREF11 .
Two feed-forward layers of hidden size 200 with rectified linear units;
One BiGRU layer with hidden size 100 using the same configuration of the previous BiGRU layer;
Two feed-forward layers of size 100 and 50 respectively with ReLU activation.
We concatenate the 31 baseline features extracted by the Marmot toolkit with the last 50 feed-forward hidden features. The baseline features are listed in Table TABREF13 . We then apply a softmax layer on the combined features to predict the binary labels.
We concatenate the 31 baseline features extracted by the Marmot2 toolkit with the last 50 feedforward hidden features. The baseline features are listed in Table 2.
Answer: The Marmot2 toolkit.
Question: If you are stranded in the African bush, what’s the best way to stay alive  ?
Answer: The best advice is to stay calm, panicking will increase your stress levels and could lead to dehydration faster.

The next is to assess your situation, look for high trees , this can help you with both safety from animals on the ground and can help with getting a better assessment of your surroundings and see if you can get a connection on your phone. 


 If it’s safe to travel, do so only at dusk and dawn when the sun is lowest. Don’t travel at night. If you need to spend the night, don’t start a fire, this could spread uncontrollable and create an additional challenge for you. If you find a tree that is free of animals and snakes, try and use a piece of clothing to tie you to that tree so you can sleep without falling. 

Finding water is essential, don’t drink from any standing water, it most likely contains dangerous pathogens. Try and find roots, bulbs or trees that have water. If you can see a river , try to follow its path downstream, it will most likely lead to more human populated areas but also be careful as animals often migrate to and from rivers.
Question: Where did the real production data come from?
Evidence: A two-step validation was conducted for English-speaking customers. An initial A/B testing for the LR model in a production setting was performed to compare the labelling strategies. A second offline comparison of the models was conducted on historical data and a selected labelling strategy. One month of data and a subset of the customers was used (approx. eighty thousand). The sampled dataset presents a fraction of positive labels of approximately 0.5 for reuse and 0.2 for one-day return. Importantly, since this evaluation is done on a subset of users, the dataset characteristic's do not necessarily represent real production traffic. The joke corpus in this dataset contains thousands of unique jokes of different categories (sci-fi, sports, etc) and types (puns, limerick, etc). The dataset was split timewise into training/validation/test sets, and hyperparameters were optimized to maximize the AUC-ROC on the validation set. As a benchmark, we also consider two additional methods: a non-personalized popularity model and one that follows BIBREF16, replacing the transformer joke encoder with a CNN network (the specialized loss and other characteristics of the DL model are kept).
The joke corpus in this dataset contains thousands of unique jokes of different categories (sci-fi, sports, etc) and types (puns, limerick, etc).
Answer: The answers are shown as follows:
*  jokes of different categories (sci-fi, sports, etc) and types (puns, limerick, etc)